Tableau Full Course 2026 [FREE] | Tableau Tutorial For Beginners | Tableau Training | Simplilearn

Key Takeaways

Hey everyone, welcome to our tablet course by simply law. Have you ever wondered how businesses make critical decisions based on data? Now Tableau is a tool that turns raw data into interactive, easy to understand visual stories. And guess what? In this course, we are going to teach you how to harness the full power of Tableau and take your data analysis skills to the next level. In this course, we will guide you from a beginner to intermediate level, helping you master data visualization, business intelligence. The demand for skilled Tableau users is soaring as businesses rely more than ever on datadriven decisions. As a result, learning Tableau can set you up for success in a rapidly growing field of data analysis, business intelligence, and decision-m. In this course, we will start with the basics of Tableau. learning how to set up tool, connecting the data, understand the concepts like dimensions, measures. Then we'll dive into creating dynamic charts, filters, and interactive dashboards that bring your data to life. And by the end of this course, you will be able to build real world business dashboards and share your insights confidently. We'll also know about the best practices to avoid common pitfalls along the way. So, here's a sneak peek of what you will learn. Introduction to Tableau and setup. We'll get familiar with the Tableau interface and tools. installing Tableau for the first time. Next, data visualization basics. We'll create bar, column line, map charts, and start building your first dashboard. Then we'll have a look at some advanced techniques. We'll dive into data preparation, data blending, explore advanced chart types like pie chart, heat maps, scatter plots, and waterfall charts. You'll learn how to build interactive real world dashboards with live data, understand data visualization, and master the art of presenting insights clearly. Then we'll have a look at the level of detail LOD and calculation where you will master complex calculations like aggregate table and rowle calculations to make data even more insightful. And lastly, we'll have a look at some tab interview questions. We'll also cover the interview questions to make you job ready. Now before we get started, if you're interested and if you're looking to advance your career in data analytics and generative AI, then the professional certificate course in data analytics and geni from ENIT Academy, IT Kpur is designed to equip you with the skills you need to succeed in today's techdriven world with master classes delivered by IIT Kpur faculty and program certificate that boost your professional profile. This course offers hands-on learning through 215 exercises and 12 plus real world projects. You will dive deep into data analysis, Python, SQL, generative AI and AI powered visualization tools like PowerBI Tableau. With live interactive sessions, you won't just gain expertise in these tools, but also earn Microsoft Azure data fundamental certification along the way. So whether you're a fresh graduate or looking to level up your skill, this 11-month course provides you everything you need to stay ahead in the world of data. So what are you waiting for? Hurry up and enroll now. You can find the course link below. Now before we get started, here's a quick quiz question for you. The question is, which of the following is a feature of Tableau that allows users to display data in interactive and dynamic format? Your options are static charts, dashboards, pivot tables, or data grids. Drop your answers in the comment section below and let's see how many of you got it right. >> Tableau as I said is also a data visualization or a self-service business intelligence tool similar to PowerBI. You're going to see a lot of similarity uh with PowerBI and we are going to see some differences as well with PowerBI and Tableau. To give you comparison of these two tools, Tableau started much earlier than PowerBI. Tableau was there in the market quite back quite earlier than PowerBI and it's very powerful very good tool when it comes to data visualization. The data visualization capability in Tableau is quite superior even when it comes to PowerBI it's more superior. PowerBI has emerged in uh about 2015 but Tableau was there earlier in uh late 2000. Okay. And Tableau very rapidly gained the market share and it evolved and it beat it has beat it had beaten all the other competitors that were there at that time in the market be it uh click or spotfire or there were other enterprise BI solutions it gained the market share quite rapidly and even right now it's among the top players Tableau used to be an independent organization. There used to be an independent company initially and then in I believe in 2019 it got acquired by Salesforce. So in 2019 Salesforce acquired it and now it is like Salesforce is the owner and you will see a lot of uh like integration with Salesforce moving forwards. Although this is an in like you can also purchase independent licenses and still it is independently available but a lot of integration you'll see with Salesforce right so for Salesforce all kinds of BI reporting work now Tableau is being used quite extensively so just once again to just give you a quick background about the different uh components different tools that we use not the tools but let's say um different types of carrier tracks and where Tableau PowerBI all of these comes into play. So you see this screen on uh this um slide on my screen. So there are these different types of career opportunities when it comes to Tableau. So there are these different roles and all of these roles would include Tableau even PowerBI can also be uh like included in these roles. Do you have understanding of what these roles are? Do you know when when we see these roles? Business analyst, BI developer, BI analyst, data analyst, BI engineer. Does it ring a bell? Do you understand what these roles are? You may be you may have heard about these roles but do you know what these roles are? What comes under these roles? Be engineer, be analyst and be a developer. Okay, let me give you a gist of the components on the project and then you will get understanding of these roles as well. So let's just start with that basic and then we'll uh deep dive into the Tableau side of things. Let me write this. So we have these roles. Let's go back to the roles. Uh business analyst, BI developer, BI analyst, data analyst, and BI engineer. Right? Let me write these down. business analyst, BI developer, data analyst, BI engineer, and which one did I miss? Okay, these were the roles that were listed there. Right, so many times organizations nowadays come up with these designations. Designations, right? So there is a data analyst position in some company. Then there is data um business analyst or BI developer position but you need to understand in instead of understanding the designation let us understand the roles because what matters is what is your role in the organization and you'll see many of these designations would have interchangeable or similar roles. So let's understand first the roles part and then it'll be clear where what kind of designation [snorts] what kind of roles are needed and even before going to the roles we need to understand that these are all part of business intelligence processes right all of these roles are part of business intelligence projects okay all of them all above are part of Business intelligence projects. That's exactly what business intelligence is. Business intelligence is a is a process. Okay. A process or a system that will always be bringing the data raw data and it will provide insights from that raw data so that you don't need to do these things again and again. So you need to uh for example let's say there is a data okay there is a data uh about let's say um uh let's say there is a like we have u a training program right a training program now we got the data about the training program let's say data is about uh how many um how many learners were there how many learners attended all the sessions how many learners had taken the certification ation. How many learners have completed all the assignments? How many learners then um uh given uh this kind of uh like uh feedback all those kind of data is available there. Now there could be a one-time analysis that okay I need to know like how did this program helped our learners. So someone would just analyze the data one time and say okay there were uh 100 participants which were there out of them uh 90% of the people attended all the sessions and completed all the assignments and then uh you got u some other uh details like these uh 80% of the people have completed the certification. Now, this is a one-time analysis, right? One-time analysis because this is a one-time event, one-time course. Um, and we got this information. But if I need to have a system where all the trainings that simply learn is providing I am able to get the information about all the courses and continuously the data for the new course courses is getting added to that system and I'm getting that information uh available to me with all these insight. So I can then I can just keep on taking the insights from them as and when the new data comes up. Right? So, so there is a one-time analysis that you can do and uh get the insight and then there is a continuous process which will keep on taking the raw data as and when the new data appears and it will show the insights in the form of visuals in the form of all the uh different types of ways with which you can show the insights right so business intelligence is a set of or it's a process a system or process using tools to convert continuously convert raw data into meaningful insights. Okay. Now we are talking about system. Okay. System or process which means that there has to be a a defined process to get from the raw data to meaningful insights. Right? And the second part which is important here is using tools. So there will be different tools or different softwares which will come which will enable this business intelligence system. Okay, which will enable this business intelligence system. So once again let us learn about the different components that will come above whenever we talk about the business intelligence project. Okay. So what are the components of business intelligence projects? Okay. So first is the first and the foremost thing is data right data. So first component is data and when we talk about data we are talking about data like what what are the sources of data. So raw data that must be coming from some source. So we talking about data source here. So where do we take the data from? This is the point of origination of data, right? Point of origination of data is your data source. Now, depending on different types of tools that you use, can you give me example? Give me example in the chat. What can be considered as the data source? Okay. Give me example of data source. What is a data source? Example of data source. Don't not the definition but an example of data source. What can be a data source? Okay. File excels. Okay. Focus on the word of origination. Focus on the word of origination and think think critically. When we say data source and the point of origination of the data, we are talking about origination of data. Right? Database is not the does database base creates data does a database creates data or does a database maintains the data is it used for just maintaining the data right so when I'm talking think about any business let's say let's say um let's take an example of [snorts] everyone everyone must have gone to some supertore right you may have gone to Walmart or Demart or you know hypers store some some store right so where does the data gets originated what is the origination of the data very like think about these these things so there are data is originated from systems like let's say there are system called there is point of sales. Okay, point of sales example. So for example, you go uh you go to hyperstore and as soon as you go there um and you you did the purchase, you go to the billing machine, right? you go to the billing um billing department and you go there they enter all the information what all they swipe they have those uh machines that scan the barcode and automatically the bills are generated right so there are these different points where the data is originating okay it can be let's say there are these pos systems point of sales system right point of sales so all these uh at the billing counter all these machines that automatically generates the data. They will also ask for your information. What is your name? What is your phone number? Some other information all those things are generating data. Okay. So there are these point of sales system that is generating data or sales. Now you may have let's take another example. Let's say that these are these also have some online presence. So people are going on the website and making a transaction that is also generating data. Correct? So you make a purchase that is also all these data are these are the data sources. So if your website is generating sales that is your source of data that website is the source of data and depending on what kind of uh database that data that website has that's where that data is getting stored whatever is being generated. So there are all these different sources. One of you said that there could be surveys. Yes, that is also a way of connecting uh getting the data. This is also data source, right? Or you are running some let's say marketing event. Let's say a digital marketing campaign is run. So how many people view your ad? How many people clicked on it? How many people then um uh visited the website and made purchase? All of these are generating the data, right? So there is let's say you ran some marketing campaign on uh LinkedIn. So your LinkedIn ads is a data source correct or you're running ad on Facebook or meta that is your data source correct or if you're talking about any kind of let's say there is an uh you you have some operations in your business. So all these operations nowadays there are those electronic machines that also tracks all the activities that is happening. So those are the points where the data is originated right. So you have point of sales data, you have uh website, you have your uh other operations tracking softwares. All of these are the point of origination of the data. Correct. Social media. Okay. So there the data is generated and sometimes you also generate data manually by some u excel files that is also there but think about data source as the first origin uh point of origin of the data. Correct? So once data is originated then organizations need to analyze that data and make some business decisions. So the second part of this is managing these data that is generated in these systems. Okay. There you need to do a system where all these different data is centrally stored. Okay. And that part is called data collection and data also data warehousing. Data warehousing there is this term called data warehouse. So when you have these different data sources, there must be a way where you can collect the data from these different systems and place it in a common one central place where all the historical data is added, right? All the data is stored there. So you can create a data warehouse which is a you can imagine it as a as a place Of course, this is uh it will be a virtual place, right? So, a place where to store all historical data from different data sources for analytical purpose. Okay, we want to analyze the data. But to analyze the data, first of all, you need to have the place where you can store all the historical data. Not just one day, two day, but all the historical data that is coming that you have generated, it should be in a big um there should be a big storage where all of this data is present and then later on you can use this data in these data warehouses to analyze the data. Okay. So data wareousing when we say data warehousing it can be a it can be a big it's considered this as a big database it's a big database. So all of these data sources data sources where the data is generated of course when we say data is generated there must be a uh all of these machines which are capturing the data that must be stored somewhere. So there would be all of these would be having their own databases where the data is stored. Correct? But that is those databases are your databases for these systems. Those are not meant to store large volume of data. They will just continuously as the activity is happening they will just store that daily transaction information. And then your data warehouse is where all the historical data and data across all these platforms are stored in one place. Okay. Once the data is stored in the data warehouse, data warehouse, it can be a big u let's say different types of u you can imagine let's say an SQL server based data warehouse or an Oracle server based data warehouse or SAP data warehouse. There can be different types of providers for the data warehouses or Azure based data warehouse or AWS data warehouse. There could be different data warehouse data warehouse um softwares that you can use and now when the data is stored in its central place you can start analyzing the data. So then comes using the BI tools. Okay. What these BI tools would do, these BI tools will connect to the data stored in the data warehouse. Okay. To analyze the data. These are used for BI tools are used to connect to data in data warehouse. for business insights data analysis right so which are these BA tools Tableau PowerBI all those are these BA tools which then connect to the data warehouse and you analyze the data okay so these are the major components And all the BI projects will have these parts. So there will be data where data source there will be data collection and data warehousing process. And then once the data is warehouse you use BI tools to connect to the data and start analyzing and giving the business insights. Now there will be different roles within this entire process and these different roles will come into where you will be like what kind of work you will be doing. So let's understand these roles. So roles in the project. So firstly let's talk about BI engineers. Okay, let's understand about BI engineers. They are also called data engineers. Data engineers is also common term that is being used here. Okay. Data engineers, BI engineers. So their role is to get this part done. Data collection and data warehousing. This part is responsibility of BI engineers or data engineers. Okay. So what data engineers do? data engineers, they set up pipelines to ingest data in from data sources to the data warehouse. Okay. So there should be uh there should be pipeline. So there should be continuous data injection that needs to happen before the injection. Of course there could be there may be some need of data cleaning transformation all those parts may be required but this is uh data. So their role is to make sure that these pipelines are working properly and the data is continuously ingested into the data warehouse. That's what BI engineers do or the data engineers do. Once the data is stored in the data warehouse, then the role of [snorts] analysts or data analysts or business intelligence analysts come into play. Okay. Next role is about analysts. Okay, analyst as the name suggests they analyzes the data which is now stored which is ready to be used right so they'll use the data from the data warehouse and analyze the data within analysts there can be multiple types of analyst okay so there can be now there can be an analyst role there can be BI developer slash data visualization ation expert or what is the other BI analyst right these are these three are same BI developer data visualization expert BI analyst they are all same their role is to their role is to use uses BI tools like PowerBI Tableau to create to analyze the data, Right? They are all same. More or less the designation names are different but more or less they are same. Okay. You can also bring Yeah. data analyst can also be added over here. The second type of role that comes here is data scientists. data scientists and the data analyst that you're talking about data analyst can fall into both BI part as well as the data science part you need to look at what the role here is because they are all they're all data analysts okay so this is mainly both of them are like this is part of data analyst okay within data analyst you will have BI side of things then you have data scientists so data scientists are more of this is this role is while BI tools are typically used to tell what has happened why it has happened those kind of things but data scientist's role is more of telling what may what may happen in the future okay so this is for more of machine learning and predicting ive analysis. Okay, predictive analysis. So, anything to predict what may happen, what will happen in the future, that is more of data scientists role would be. Okay, both can come into the data analyst. Under data analyst you can have uh BI developer data visualization that is the first one which uses BI tools and then there are other which uses predictive and they use the tools like mainly Python or SAS. Okay. And nowadays there are all these uh cloud-based there are existing uh algorithms that are there which can just be ready to deploy those kind of things are emerging but these are the typical tools that data scientists use. Okay. So this is more about analyzing your data. The first one analyzes what has happened in your data so far. What is what does it tell you? And the second one is what is going to happen in future. what may happen okay within this there is then another role in the project which is business analyst so if you see the first role I did not mention Python anywhere right so here you can use the BI tools and you don't need to learn and you need to apply Python here okay this can be done with the BI tools like PowerBI Tableau even if you know one of the tool that is good enough And another tool that I should add here is I should add this is SQL. Okay, SQL is also important. So if you know these then you're good for this role first role. You don't need to learn anything else. Right? So this these are the components of a BI project. Okay. So a component of project BI project. I'll come to the hierarchy part. Let me uh I'll come to that. Let me answer that in a while once we discuss this you'll get clarity on this. These are some of right now we are discussing about different components in okay so we have one we have the data engineering then we have the data analyst okay so so far these are the one roles which are executing your project okay execution of the project is being done through these roles correct so the third role is more about understanding the business problem so the other role which you may have also you are also learning which is the so the third is business analyst. Okay. Now many people get confused with business analyst and data analyst. These are two different roles. So business analyst are the people who analyzes the business requirements. Okay. They analyzes the business requirements and then they plan the execution. Okay. What needs to be done for a business pro for a project, right? So business analysts are work on the project requirements and collaborate with data engineers and data analysts for project deliverables. Okay. So, business analysts are these are the ones who don't deep dive into the technical tools but they need to know both the sides of the of the project the business side like what the business requirements are. So, if there is a project then what is the objective? What is expected out of that project? What kind of uh what can be delivered, what cannot be delivered, what will be the eventual um uh project that you are going to be delivering to the client, what kind of u business intelligence, reports, dashboards, [snorts] um any kind of deliverable. So that is defined by business analyst. So business analyst work closely with data engineers and data analysts to make sure that projects requirements are understood and delivered within the scope of the project. Okay, that is the role of business analyst. Business analyst talk to the client stakeholders like the clients and they take the requirements. They ensure that everything is what the client requires. So we are delivering that okay that is a business analyst's role okay so business analyst often work with the data engineer data analyst they guide data an analyst and data okay yes what you're saying is correct so you may have seen opportunities where there are these roles where SQL powerb skills are required for business analyst That is what nowadays organizations require because assume that you are a business in uh analyst. Okay. Now if you're working with BI developers who know SQLBI, PowerBI and Tableau and they tell you that okay this work is going to take 90 days to complete. If you don't know about these tools, even the anything about these tools, you're going to you're going to uh like believe that what they are saying, right? But if you know at least the basics, if you know these tools a bit, you would be able to also think critically and say that okay, it does not look like you're giving me the right numbers. Maybe this can be done in not 90 days but 60 days. If you know some technical skills then it will be better for organizations and that is why organizations also say that you should have these skills then you will be able to work better in this role. Okay. That's why these skills are required. Okay. Another role that would come here is project management or manager that may come. Okay. So project manager. So there is a business analyst and then there is a project manager. Project manager need not to have any technical skills when it comes to the BI part. Although nowadays like if you have some technical skills that is always advantageous but project manager ensures all client expectations are met, right? All clients expectations are met. be it in terms of uh the what is um I would say all the client expectations are met and ensuring all resources for the project right so whatever is required for the project so all the uh everything B is related to the software be it related to the team members be it related to the workforce be it related to overall uh budgeting costing all those things. So all this part which is more of ensuring everything is met for this project. So that is done by the project manager. Okay. Now Ron you were asking about the hierarchies how the hierarchies are are there. So this is exactly the hierarchy would be. So there would be in a team there would be at the very top there would be project managers right then below that you will be having BAS right and then below them there would be data engineers which will work which are at the same level data engineers data okay so there would be these roles now within these there could be senior Senior data engineer, senior data scientists and then there would be junior data engineers, data scientist those kind of of course the depending on the experience those kind of hierarchies can also come but these are the role wise hierarchy would be. So there will be project manager then data analyst then your data this is how the hierarchy would be right now for the BA role if you know the capabilities of the tools if you know um what can be achieved with these tools what kind of uh uh tools capabilities are even if you're not going into much of the self-implementation part. Even if you know that that is good enough to start with but then the more you know the better you become. Okay. So this is this is just to give you a context of where everything plays out. Now let's go back to the this course and here we are talking about Tableau and let's just understand what we are going to learn about learn in this course. So we are on the data visualization expert or BI developer, BI analyst. All of these are same thing. So we are within that role we are learning this tool, right? We're learning Tableau for that purpose. So what are the outcomes for this project? What we are for this of this course? You will be able to use Tableau in your work. You will be able to apply Tableau's different types of visuals to analyze the data, different types of um ways to present the data. Okay. Different types of interactions, filtering, data blending, all those detailed analysis parts. You'll be able to create charts, dashboard, stories, all those. But my focus would be here to understand the data insights. So [snorts] whatever that we do there needs to be some logical thinking right and there needs to be some some critical thinking behind what why we are doing this what why it is better why it is not how we should be doing. So those kind of things we are going to consider. Okay. So along with the technical learning we will also understand these parts. So these are some of the course outlines that we have. Now data visualization this is Tableau is very good when it comes to data visualization. It's very powerful and data visualization is as we have learned in PowerBI as well data visualization makes it easy for the end user to understand what the data is telling you. Right? So I'll take couple of more minutes just to give you some feel for it and then we'll begin to enter the actual uh tableau side of things. For example, these like if you have gone to historical sites, you will see these kind of carvings in the caves, right? All these depictions, right? So even before the languages were there, even before the scripts were there, there was data visualization and this was a very powerful way of telling story. So these are just the some examples of in the like historical times uh people used to just put some visualization just to tell the next generation how what they have done, how they used to survive. So these kind of you'll see a lot of these kind of visualizations in historical sites like this is uh this telling you that how they used to hunt. So what kind of weapon they used to do they used to have right all those things there will be many other details that you will see when it comes to visualization in the ancient time right these for example these the through visualizations you will see even the people have shared what kind of culture they are coming from so how they have festivities how they have uh how they hunt, how they uh what they pet, how they communicate. There are a lot of things which are depicted through visualizations earlier. Okay, this is a famous visual for created by this artist called Minard map. Minard was the artist and was created this one map and this is a visualization of how Napoleon's march like how did it the overall troop size how they have reduced. So when they started off there is a timeline to that when they started they have taken some a route and then as they marched to Moscow they lost their fleet they lost their troop in the midway and you will see this is shown with this illustration. So this is the route that they had taken while they were marching. And this is the route that they have taken while they have come back and from where how many people went. This is depicted by this uh brownish color to how many people survived and came back is shown by this dark black color. So this just one simple visualization tells a lot of details. You don't need to really this is itself is a story. It tells a lot of things just one visualization. Okay. So there are many examples that you will find uh in this for example periodic table. Everyone I believe from their school days remember the periodic table. This is also a visualization but see how the elements are put together in this visualization. Correct? Yes. Chemistry everyone must have seen this. This is telling this is telling so many things you will you are able to see all the elements that exist. Okay. Then based on their properties based on their nature they are they are created in such a way that it tells you a lot of details just by visualizing. So anything which is coming in this section they are I think um even I'm forgetting my chemistry lessons earlier but you have your uh metals in the right in the left hand section right you have um um even I'm forgetting um you have your you used to have your inert guesses if you remember right you have your inert inert guesses in the last column which are non-reactive right so they are colorcoded in a different way these do not react very well then the these ones which are in the very left they are the donors they donate electrons to make bonds these are the acptors they accept uh uh electrons to make bonds so those kind of details are there right all those details are shown in the periodic table and then you have other information like what is their atomic mass, what is their atomic uh all these details, right? This is also a visualization and you'll see everywhere lot of places you'll see the example of visualization. Okay. And Tableau is a great tool to visualize your data. Why do what do we do essentially with the visualization? What exactly we do? Now the process of installing Tableau in your Windows. So if you're using Windows operating system just open Google and once you open Google you just have to type install Tableau and then you will have the following web page. So once you get into this web page you just have to get yourself registered by adding your first name last name all the details business or personal email if you're working for an organization and add that size of the company department job role country etc. phone number and then you can download a free trial. Once you download the files, you just have to go through the installation procedures and you will be fully ready with your Tableau desktop which looks something like this. Right? We already have some data here. You don't have to worry. We will restart Tableau and try to import our data freshly. So there you go. Once you get started with Tableau, this is the overview we will have. So Tableau basically has some open-source data dashboards for your learning experience. So you also have some tabs here where you can get started with learning Tableau and you also get some and explore some sample data sets and everything. Now we will be dealing with this particular one get started with a data source. So Tableau is just like any other data visualization tool which can access data from anywhere. You can get connected yourself through a database. You can get connected yourself to a website, social media platform or anything. And if you want your data to be in uh Excel format, you can also get connected to a CSV or an Excel data file or anything. Right? So if it is data then you can access it. Now we will be dealing with the Excel files. Right? So our sales data is present in this particular data set. So we have product data, delivery data, customers data, rent data. So what I have did is I've combined all those into one complete workbook which happens to be our sales combined. Let's have a quick overview of how it exactly looks like. So this is how our sales data looks like. The first tab which happens to be the vendors tab which is the person who is selling the product. So he has the order ID, order date, delivery date, traffic, leads etc. Right? So uh before we have a quick walk through about this data, let's try to create a sample data. Right? So so we are making use of GPT here. So this is the prompt. So what we are writing is we want four data sets each of 5,000 entries. So the four data set names are vendor, customer, order and delivery. Right? So these are the data sets and we are looking for these columns. Order ID, order date, delivery date, traffic, leads, orders, revenue and here we have another column. So you can see order in all the four data tables. So there is a reason behind it. So this is this acts as a primary key which helps us to create a join or create a relationship between the other tables right and here if you see we have order date and delivery date together and here in the last table last one delivery table we have booking day and receive date right so these are one and the same but we are maintaining a different name because this will help us in understanding what exactly is data blending right data blending is nothing but the same data in between two different tables shared among two different tables but with different column names. So sometimes if Tableau is able to understand what exactly is happening then it will automatically establish a relationship between two different tables. But in case if it doesn't understand by reading the column name in case if there is a different column name in different tables then it might not establish that content or connection. Right? So in such scenarios you can have to go with the custom connection between two tables by linking the data columns. So in this case we will link order date with booking date and delivery date with receive date. Right? And the last table is here. So which will be having product ID, product category and quantity. So we're also adding some category names here and product names and quality quantity. Right? So how many number of products have been ordered right and we are taking this data from a common man's POV right so we will quickly copy this prompt and we will drop it on the window of chat GPT so we using a premium version here so quickly we should be receiving the response there you go so we have two different responses here and uh here you can select any one of those and quickly download in the form of CSV or as a workbook so we have already downloaded that particular data said and you have clubed them into one. Right? So we have the same data and we have distributed them in four different sheets. So these are the ones. So we have every column that we gave into the prompt. And uh here you can see we have revenue and company. We did not calculate uh profit which happens to be revenue minus cost. So that we can do this practically on Tableau to understand the calculated fields. Now that we have a brief understanding about our data set, now let's begin with creating storyboards on our tableau. So there you go. I'll select the sales combined workbook and open it. And shortly we will be having the data set ready. So here we have it. Now as you can see we have four different data tables. Now to establish a data model or to establish a connection between two different columns or to join them all you have to do is just drag the data set here and leave it. And after that whichever table you want to join with the first table just drag and drop. There you go. You have the first connection. This particular pipeline or a string type connection is nothing but a connection between the two different tables. Right? They have established it. And now let's connect this again. This one here. So I think we drag delivery twice. Let's drag product. And the last one which happens to be the delivery uh not the vendor. Right. There you go. So there you go. We have a quick good connection. So uh Tableau has identified the data. So if we quickly go to the data vision sheet here. So initially we had a booking day and uh delivery day receive date right so it has understood the format and it has mentioned it as a calendar format here and order date and uh booking day are recognized as one of the same and we have a proper connection established here right let's say I want to create a join between two tables let me quickly drag and drop all the tables here now let's say I wanted to create a join Now you double click this particular table and now drag delivery here and here you can see there is a join an inner join. If you want to modify that, you can also modify that to a full join, full outer join, right join and left join. And here we can see Tableau has automatically recognized on what basis it should create a join which is order ID from the first customer table and order ID from the second table which happens to be the delivery table. Right? Now we don't want to proceed with the join for this particular data set. We will just simply go ahead with data modeling. Right? Let's quickly model the data back. I have customers data here, delivery data here and product data here and lastly the vendor. There you go. Our data model is fully prepared now. Now what we can do is go back to sheet one and get started with creating story lines. Now let's say I want to identify the total sales based on regions. So what we can quickly do is you can add regions to columns. You can add order ids to rows. There you go. So what we did is we quickly changed the aggregation of order ID so that it counts the total number of orders present in that particular region. Right. So we have four different regions here. So it has calculated total orders that we received from all the different regions. So to improvise this you can also add data tables or data labels. So what you can do is uh drag and drop order ids to label and here the aggregation can also do that. So that tool tip you you just have to add it to the tool tip should be sum. There you go. Now you have the count of orders from three different four different regions. And in case if you want to make it a little more colorful, you can also add region to color so that each and every region has a different color. So that's the first story that we made a region wise sales so that you can understand which is the highest uh profit making region in your country. So here I can see uh northeast region is giving us the highest sale here which happens to be 31,000 no 31 lakh 96,296 sales. Now let's create a new sheet and now let's understand the sales based on category or product. Right. Now let me add category to columns and the number of orders that we received based on those particular categories. Add all members and you might have to change the aggregation. So here it is measured to sum and you have u what do you say the bar graph here but in case if you don't want the bar graph you choose to have something else than a bar graph then you can go with the pie chart here and here you can change the standard size to entire view and you have it here and just like we discussed before you can add order ID to label and change the aggregation. Yeah it already changed. Okay. Yeah, there you have. So, change the aggregation to sum or count and you have the total number of orders from a different category. And if you want the category name also to be popped up, you can also add a category into the table. Now you can see we have medical category has so many number of orders, furniture has so many, auto parts have so many and electronics has so many number of orders. And you can change the sheet name to category wise sales. Right now you can create a new story again. So since you're dealing with American states, so you can also do a statewise sales report. So u where you have the sales number. Yeah, you have the sales uh detail here. You can add it to the columns and uh number of orders or let's say we will go with traffic. Let's see which country has the highest number of traffic. Now since we have you can see that the state thing is visualize a geographic location. You can choose a map here right you can choose this type of map or this one. So this is one of the recommended ones. I'll go with this one. And it might take a little while to read the data and give us the numbers. And you can also add a state to text label here and uh also some of the traffic. That's okay. It might take a little while to reflect on this particular map. So that's how you do the statewide sales. And let's go through the data set once again and see what kind of reports we can move ahead and create. Right now we have a product reports here. Let's go with the product sales as well. You can drag and drop product to rows and uh the leads and products here. And you can choose a map chart tree map. There you go. So you can also add leads to label. Just drag and drop it here. And now you can see we have uh medical. Okay. We will also add category, right? Drag and drop. And you can add it to rows to split the category in four different formats. Or you can drag and drop categories into columns to make it look like this. Right now you can see medical is the highest one which is giving you the total highest number of leads. So you have 34,000 number of highest leads from medical category. In a total it is more than about 65 or 67,000. Right? That's how you can do it. Now let's keep it this way. Now we can term it as category wise sales. Right? Now let's quickly create a new sheet. Now as discussed let's try to also venture into the calculated fields. Now if you quickly take a look at the data we have revenue we have orders leads and everything and we also have cost of the company and revenue. But if you closely observe we don't have something called as profit right you might be spending around 10 rupees to buy a product and 15 rupees to sell it. So you made about 5 rupees of profit. Now did you see the column named as profit here? No. So how to create that uh new column header which is turned at profit. Now uh let's quickly double click this and double click this one as well and uh go to a table type appearance. Now you can see what's the cost and revenue. Now I wanted to create a new column which is termed as profit. Now click this particular option which is a small arrow kind of thing and here you can see a create option. go to a field called as calculated field. Right now we want this to be named as profit and uh if you are unsure about the functionalities it is about to offer you can have all this here right it you can work with different data types let's say I have since we already have delivery date and audit date we don't know how many days it took to deliver the product let's say you wanted to find out that you wanted to find out the difference between the delivery date and the audit date to know what are the number of days a particular vendor or a particular delivery guy is taking to deliver your product from your end to the customer. You can also do that. Right? For now, we want to go with the number one. Right? So here we have uh multiple options. You can go with max, minimum, power, radians, and you can also go with trigonometry things. Right? Now I want to calculate. You can uh you know access this using the small uh menu icon here. Right? You can do that. So if you don't want you can just quickly continue. Now here we want to do something uh called as uh sum of uh revenue. Here you have it. You can scroll and press tab minus sum of cost to company and your formula is done. So in case if uh there are errors, it will say uh the calculation contains errors. So we might have the error over here. Closing parenthesis. Okay. Yeah. So we did not close the parenthesis to this one, right? Uh this is because we manually typed it. But in case if you had taken the recommendations from Tableau, it have got something like this which already has a closing parenthesis over here. Now we want to subtract revenue minus cost to company. Right? So again scroll down and press tab to select and there you go. Now the calculation is valid as you can see here. And now you just press apply and your okay anything. And you will have a new column called as profit. Now let's understand let's remove these both from here. Let's remove this one as well. Now we have a new sheet fresh sheet. Now we want to identify which category which vendor. Yeah. Let's say we want to identify that one product which is giving us maximum profit. Now what you can do is you can take the product. Let's quickly check the data once. So here we have the product data. Now here here we have category and product. Right? So we want this particular one the product and want to find out which is that one single product which is giving us the maximum profits. Right? Now let's go to the product uh table. Here we have the product. Select the product and drop it around the columns or rows wherever you want. And now we have recently created a new calculated field which happens to be the profit. Search for it. There you go. We have it here and drop it right here. There you go. Now if you would like to go with this particular one which is bubble chart, you can also do that. And you can also have the aggregation of profit to the text label here and change aggregation to sum. There you go. We already have the table here. So you can see tablets and syrups as we have this here category by sales. So the category was tablets instead of the medical category was the highest selling one. So here obviously we have the highest selling ones are these two ones right and something from furniture is not performing well right so here we have it 48,000 happens to be the least one and so on right now let's quickly do it uh product product wise profit so far so good so we have about four to five sheets and now we can quickly go with the dashboard with some KPIs. Okay. Now, what you can do is just quickly drag and drop those sheets onto your dashboard and make some adjustments to it. You can close show me and here you can choose um standard and the size of your dashboard. You can also program or make your dashboard such a way that it can be visible in phone as well. I don't want the phone now. I want to go with the default one. And here you have size of the canvas. You can increase the width and height of your canvas. I increase it. There you go. So far we have uh installed region wise seal. You can also you know uh change the size of your charts here. Drag and drop state wise sales and you can drag and drop category wise sales, product wise profits as well. So there you go. So after making some adjustments right in the width wise or the lengthwise you can adjust u the dashboard and one thing which is missing here is the KPIs. So we created a new uh sheet here and added a KPI termed as leads. Similarly add another sheet and this will be your revenue. Add revenue to rows or columns and you can choose a table here just like we did before. And you can add um to tool tip. And u similarly you can add orders and graphics as well. And this will be named as orders. This will be named as traffic. So drag and drop traffic to the rows or columns. Select table and add traffic to tool. Similarly orders. drag and drop orders to rows and this should be a card and orders to total. There you go. Again, we will go to dashboard and here you can drag and drop the cards that you recently created. So, I want the leads to be here. Don't worry, we can adjust uh the cards. There you go. So after making few adjustments to the title we can get the last one apply this particular one as well let's keep it as 10 so that the numbers are visible lastly this one so you have the KPIs region wise sales state wise sales product wise sales category wise and product wise as well so this was supposed to be uh product wise sales Here we have power product wise and category wise both. Not a problem. And here you have if you close the show me icon. So here we have the filters and slicers. So in case if you wanted to check for the sales happening in only Middle East from category of chairs and uh product chairs and category as furniture. There you go. So you have the total information on that front. And if you can just quickly release all the filters you have that. And if you wanted to know the sales of dining table, here you have it with all the related information to that particular item. So that's how you create a completely interactive dashboard in Tableau. Okay. So everyone please go ahead and download Tableau public. I've shared the link here. Once you land on this link, once you land on this link, it'll ask for some details, right? This details you need to put in. Okay, just mention these details. It does not matter if those are accurate exactly accurate like like your role. You can put any role like student, you can put your location, just put all these details and start downloading. Let me know once you have downloaded. Installation is very straightforward. Don't do anything customized. Just do the standard installation. Okay. So I'll allow you to do this part and let me know everyone. I want everyone to give me a confirmation once this Tableau public is downloaded and installed on your machine. All right. So when you open Tableau public here, it'll open like this. You will be able to see a screen, right? So at the center you will have currently it will be all blank for you. But for my case it'll it is showing some of the recent files that I had worked on. Uh you have some section at the right hand side some uh links some tutorials some other favorites some other links available over here. Then you have the data connection options at the left. Okay. So uh in Tableau public you have these file type connection options. So you can connect to the Excel, text, JSON, CSV, um, uh, access file, those kind of files you can connect to and then you can connect to some servers like O data that we have seen and only these Google drive. These are the only connections that are available in Tableau public. with Tableau Desktop. If you were working on Tableau Desktop, all this entire area would be filled with all sorts of data connections. So there are many data connections available. With Tableau public, you would be able to see all with Tableau Desktop. You will be able to see all those. Okay. Here all different types of data connections. you can connect to all types of databases uh some systems some other types of platforms that you can connect to. Okay. So to connect to any data we can just use these connect option. So from the data sets that you have downloaded there will be a download there will be a file called global supererstore data. Can you all see that global supererstore Excel file in the downloaded data and in the inclass data sets you should be able to see sorry there is the file name is sample super store data sample super store data okay so we'll connect to this global sample super store data here from with Tableau public so what you need to do is this is an excel type data file so You connect click on Microsoft Excel connector. Okay. Go to Microsoft Excel connector. Then you search for this file and click okay. When you connect to the data here, you will be navigated to this screen. Okay. This is where you are able to see all the tables. So within this sample super store you will be able to see all the underlying sheets as separate tables. Okay. So you can see these sheets over here. Okay. You have these sheets. Okay. All these are coming from this one connection. Okay. So this is the connection that you have made. Within that connection all the sheets. If you are connecting to a database it'll show all the tables which are available there. If you're connecting to a file, it will show all the sheets which are there. Okay. Now, whichever table that you need to analyze here, you can simply drag that table. Select, drag and drop it on this section. Okay. Drag orders. For example, drag orders and put it in this section. Just drag and drop it over here. Okay, drag and drop it over here. Now let's see what is what all is there. What is all of these section? Let's just segregate and understand these sections what are there. So here this pane is your connections pane. This is connections. Then this is your this section is also called as the logical logical phase. Okay, this is your logical phase. You call it logical phase or your logical layer. Logical layer. Okay. Then whatever data that is there inside this table, you can see the preview of that data in this section. So there is this data preview. Okay, let me pull this slightly up. This is the data preview. Okay, this is the data preview. In this data preview, you are able to see all the columns that are there. Okay, so you have the column name. Okay, column name, all the column preview values. Okay, so not the complete data but just the preview. And it shows you at the top that how many rows you are seeing in this preview. So currently 100 rows are being displayed. Okay, if you want to increase more, you can do so. But at the moment these are showing 100 rows of your data. Top 100 rows. Okay. Here we can see the column name and we can also see what the data type of the column is. So you have the column name and you can see the data type. How can you see the data type? You see this these symbols these icons this is telling you what the data type of this column is. So data type hash means that this is a number number data type hash is a symbol for number. ABC stands for text. If there is a text data type similar to power query if you remember power query we had ABC 1 2 3 right? So ABC denotes text. This calendar like icon this denotes date. This is a date data. Shape date. This is also a date and so on. If you want to change the data type, you have this small drop-down next to this. Okay. Sorry. If you have to change the data type, you can click directly over here. Not the drop-down, but you click over here. So you can see all the different data types that exist. So number, whole number, decimal, date, date, time, string, boolean, those kind of data types. If you ever want to change the data type, you can do it from here. Below the data type, you can see the table name. So these all everything is coming from the orders table. So the table name and below the table name, you have the column names. Okay. So you can see the data preview. Then next to this data preview there is this metadata section. Metadata section is called metadata. [clears throat] Metadata. Okay. So metadata is yes it is data about data which means whatever data that you have some information about that. So some basic information like not actually going into the data level like the actual data values that you have in the uh in the table but just the overview of your data like what is the name of the table? What is the name of the fields? What are the data type of those fields? Whether this exists on the physical level physical layer or not. So it says that this is physical as we see logical sorry this is logical layer but this is physical physical table physical table we have actually added this table. So this is the physical table then there are remote fields. So remote fields is if you are adding any other aliases to your field names some other names then that kind of data is added. So basically your table name your data uh all the column names the data type of the column those kind of information is your metadata. Okay. So that information is available over here. Okay. Okay. Your connection interface this is how it looks like. Let's say now you are okay with what data you have and now you want to analyze this data. You want to start analyzing the data. Okay. So in analyzing the data you can go to here you see go to worksheet. So here in Tableau we have the concept of worksheets. So to do any analysis you need to go to worksheets and then start analyzing the data. Okay. It says that go to the worksheet. Currently this is the data source uh view that we have. If you click on this sheet one. Okay. This will take you to the worksheet. Okay, this is your worksheet. In the worksheet, you do the data analysis part. Okay, so here the data analysis would happen. Exact. Okay, we'll deep dive into the worksheet part. But before going to the worksheet, let me show you just a few features that you can use while you are in the data source page. So we'll come to the worksheet later. Now let's get back to the data source. What else? What all we can do here. [clears throat] Now let's say I want to look at this data preview. And in the data preview, I want to just see whether I need to use any columns or remove the columns. So I want to see this in more like I want to see more of this data. I can just collapse this metadata part. So just this small arrow just collapse this. And now you have this table details right or the data preview. Okay. Now in this data preview uh here I can choose if I want to hide or unhide any columns. Okay. So whatever columns that you're keeping here that will be visible in your worksheet view. So if you go to the worksheet view once again table columns all the columns are displayed over here in the left. Okay you will be able to see all the columns over here. Now let's say that you don't want some of the columns to be visible over here. Those are not required not will not be used. Then you can simply go and hide those. In Tableau we cannot delete the columns that is a drawback. Okay. So if your table is containing that column like we have in PowerBI the the good part about PowerBI is that you have power query where you can delete anything that you do not require and those data gets loaded. But here we cannot delete them but we can hide anything if we don't want. Okay, that you can do. So any column if you want to hide, you have this small arrows. Let's say I don't want to see the let's say I'm not interested in looking into this um postal code or region column. Those I do not really require. Okay. So I can go ahead and from this small arrow I can click on hide. Okay. Postal code. Let's say I'm hiding the postal code. Okay. Now postal code is gone here. It is hidden. Remember it is hidden. It is not deleted. It is not deleted. It is just hidden. If I now go to the sheet, okay, sheet now here I no longer have the postal code column. It's not deleted but it is hidden. Okay, I can hide multiple columns here. But if I want to bring them back, if I need to bring it back, if I need to see all the hidden columns, I need to go to this setting, this gear icon over here. You see the gear icon. Click on the gear icon. And here you have the option of show hidden fields. Okay, you can click on show hidden field. It'll bring back the postal code. But you see after unhiding the field, it is still coming in this gray text. So this is grayed out. This is still not fully unhidden. Okay. If you still go to the sheet, this will still not be visible. Okay. It will not be visible. Okay. To completely unhide it, you need to from that column once again in this drop-down, you need to once again click on unhide. Okay. If you click on it, unhide once again over here in the column, then this become completely unhidden in the worksheets. So when it comes to unhiding, you can show the hidden field on this table view, but those will still be hidden in the worksheet. But if you want this to be visible on the worksheet as well, then you once again unhide it in this column properties. Okay. Now if I go to the sheet, I'll be able to see the postal code. Okay. This field is available here. So remember hiding is a one-step process. Just one button hiding. Click and hide. This is one step. Okay. But unhiding requires two step. Okay. This is okay. There seems to be some glitch. When you hide it directly from here, it should just be removed here. But it is maybe storing it in the cache. Okay. Anyways, you can unhide and hide the values like this. Please check at your end. Let me know if you have any question. This should be quite straightforward. Okay. So, you can hide and hide. Now, if you want, let's say you want to give a alias name to your fields. Okay. You want to give some aliases. You can also go and you can give rename and you can give the aliases. Okay. For example, if I need to rename this over here, postal, let's say I change this postal code. Okay, just double click over here. Let's say I change it to instead of postal code, I change it to the zip code. Let's say I give this name. Okay, postal code now updated to zip code. Okay, this is now updated to zip code here. If I go to the table, if I go to the worksheet, I would be able to see zip code. Now the new renamed column. So double click and you can see the and if let's say you have renamed multiple columns and you want to see what all columns were renamed, you can go to collapse this table view and see the metadata. Okay, you can see the metadata. in the metadata you will be able to grab this information. So you see now this tells you if anything is renamed. So now you can see this field the field name is zip code but the remote field the original field was actually postal code which we have now renamed to zip code. So you can track this if anything is renamed you can see those details over here in the metadata. Okay. No. So beat any BI tool if you are using the data if you're connecting it does not writes back the data into the source machine source data source level. It only reads the data. So we are just reading the data from the sources not writing. Okay. So even all the name changes, data value changes, all the aliases changes, everything that happens in this layer after reading the data. Okay, that is happening only in the tableau level. So we can change the column names. We can also change these values. So let's say we have these columns. Now if I need to give some other names to the values, I can change the aliases. Okay? So for example you have these regions and you want to change the aliases. If you want to give some other names let's say you can go to aliases. In aliases you will be able to see these actual member values. So the unique uh distinct values that you have in the column. And if you want to give some other name to this you can give the aliases to those. You can double click on this and you can give the aliases some other name to those. Okay. So you can change some values like this is similar to how we have the replace all replace values feature in power query. You can give different aliases. It's called aliases in Tableau. Okay. for any column if you want to see more details. Okay, I'll cover the aliases as again. Let's say you want to give a specific type of naming to your data. You want to update the data. Uh for example, you have these categories. Now you have categories like furniture, office supplies and technology. If you go, you can check the aliases. Okay. Or before we go to the aliases, let me explain you one more thing. Then we'll come back to the aliases. You can actually if you want to see more details about your column, you can go to describe. Okay, go to describe. It will basically give you more information about your field. Okay, so what is the name of the field? Some details over here we'll cover. But here basically it shows you in this field these are the distinct values that you have. Furniture, office, supply, technology. Now if you think that okay one of the field name needs to be updated everywhere. Let's say you want office supplies to be changed to a name called office goods. Let's say you want to rename this to this everywhere. Instead of supplies it will be office goods. Right? So if you want to do that you can go to aliases. Go to aliases and where it is office supplies just rename it to just type office goods. Okay. Now you have given an alias to the office supplies and wherever you have this s star symbol this means that aliases has been given. Okay I have given an alias. Now I can like okay now this alias has been given. Now you see you have office goods furniture and technology. Right. I have just renamed this field value. Okay. On this setting you can also see that you have some sorting options. You can sort the data uh any columns sorting order in the ascending order, descending order. Those kind of sorting you can do. Okay. But that is typically no is not done. So you don't do the field sorting like this. There's no need for that. Okay. So for example uh just to cover this field sorting let's say you want to sort the fields in the A to Z order. Let's say I did it like this. So now all these field names now you see that anything starting with A comes first then B C. So it'll come in the sorted order of the field names. Okay. This will help you find the fields easily. That way it can be helpful. So once when it is sorted if you go to the sheet if you need to like work on anything you know if anything is starting with T or X or W it'll most likely be at the bottom right anything starting with A2 ABC D that'll be at the top so just finding them would be easy if it is sorted okay that way it is helpful about the uh this hiding unhiding part. So one thing that why you were seeing that uh the hidden fields were visible second time because this show hidden fields was actually checked right. So show hidden fields were actually checked that's why it was appearing. So if you un uncheck this then once you try to hide it it will not show. Okay. So it's not like first time it will show second time it will not show. Okay. For example, if I remove this show hidden field. Now, if I try to hide this count, it'll be hidden. It'll be hidden. It'll not show up. Okay. You need to once again show hidden field. Then unhide. Next time if you once again select deselect this and you try to hide this it'll go it'll be removed. In Tableau, you don't have a lot of data cleaning and data transformation features like you have in Power Query. Right? In Power Query, you can do a lot of things. In Tableau directly in Tableau Desktop, you don't get that feature. There is a separate tool of Tableau which is called Tableau Prep. That is like that tool did not pan out that much because that's a separate tool. It does not come as a built-in addin in Tableau. So that's why it did not picked up that much. But there is a separate tool called Tableau data prep or Tableau prep. Okay, that has much more features of data cleaning and data transformation. And some I don't I haven't seen many organizations using it because if you're using a second tool then maybe it's better that you do it at the data source level. You can do that cleaning and transformation. Okay. But here you have very basic things like you can do this column splitting part. Some of these parts for example this customer ID here you have a feature called you can do a column split. So if you want to do a split you can create a just do a split or a custom split. For example here you have a delimiter right? So you have the customer name, the first name and the last name initials are here and there is an unique customer number splitted by the delimiter. You can simply click on split and it'll automatically identify the delimiter and it'll split the column into these two columns. The customer first name initials, first and the last name initials and the customer name. It automatically identified this. Okay. So wherever there is a clear delimiter that you know that this should be uh separated there is only one delimiter then it'll automatically do this right that is feasible otherwise you can also go for a custom split. So when custom split you go to custom split you specify what the separator is is what from which separator do is separate all the instances or first or the last you can do all those. So you can define more customized way of splitting the column with this custom split. Okay. the fields which are calculated. So even though we are doing some transformation but whatever field which are calculated you will be able to see the this indication over here. So any field which is an output of a calculation you will be able to see this a calculation text to it. Okay. This way you can identify anything which is a calculation. Okay. Although we are doing a just one button-based split option but actually there was a calculation that has happened and that calculation resulted into this output. Okay. So how do we know any field which is a calculation? There are two ways with which you can know. One is it will have this text calculation number one. Number two, even the data type that you have the data type will have this equal to symbol and after the equal to symbol you will have the data type. So whenever you have equal to symbol and then the data type it means that this is a calculation and after the calculation the data type is given to this. Okay equal to and then ABC 1 2 3 or equal to then hash. Okay, this is the calculation it this is how we can see the calculation. If you go to the worksheet where the data is visible for you to analyze even there you will be able to see this field with this data types and equal to and data type. So from here as well here the calculation word is not written but just by looking at this you will be able to identify that there is a calculation. Okay. Now for anything which is calculated those can be deleted. So I can go and delete those. Anything which is a calculation that can be deleted. Anything which is coming native from the table will not be deleted. So these can be deleted. Okay. Now these calculation are deleted. So how did we do this? Let me repeat once again since you are asking. You go to this option. You have the option of split. just do direct split. You do a split, it will identify some delimiter on its own and it will split the values. So it identify this dash as the delimiter over here and split it. You can rename these column names as well. Right? We learned this. Okay? So see uh for you when you are splitting it, it is coming at the end. So this is because you have not I have done this data sorting. You remember on this setting I chose that data to always be column to always show in this order A to Z. Okay. Since your data is not in the any order that's why it is coming at the end. Any new column is coming at the end. So change this to the A toz sorting. A toz descending or ascending sorting. then it'll come next to it because the initials would be same alphabets it will come next to it. Okay. If you have created even if you have created multiple columns everyone you can just select multiple columns and delete those. Okay. Just select multiple columns and delete. So all these calculated columns will be coming at the end. Right? So you can just do a control and click. This will select all the columns. Make sure that you are selecting only the calculated columns not the existing one. Once you select multiple calculated column, right click on the column and click on delete. It'll delete those additional columns. So this split is very simple. This is one button click, right? Just a button click split button. It will be it will just create new column with this calculation. Okay. So there is a calculation which is going behind these new columns and we're going to learn more about creating new calculations later but there is a calculation which is happening and we can edit those modify those calculations there is option. So if you just want to see what is that calculation you can go to this and you can go to edit. If you go to edit it will actually show you what exactly the calculation was to do this step. So this is the calculation which is happening. So there are this trim function, split function, all those are being used for this automated step to happen. Okay. So you can do edit those here like in PowerBI power query we have the merge columns option as well. But here directly we don't have that option directly in the columns to merge multiple columns. We can split but merging directly is not possible. For that you need to write a merge formula which will merge the columns multiple columns into one column. Okay, that you cannot do calculation again calculation is if you want to create a new calculation using the existing fields those are called calculation. Okay. So calculation would require you need to write a formula and then whatever formuladriven new values are coming those are calculations. Okay, in PowerBI you need to understand this. We used to use DAX for the calculations or in power query as well we have the calculation feature right where you we can create new column new measure right those kind of things here we don't have that distinction that create new column create new uh new measure those kind of segregation is not there in Tableau it's just a new calculation new field calculation that's all okay new field calculation okay now let's go back. I'll just delete these new calculations column split. And now we'll just take a look at the window in the worksheet and what does that what exactly do we have in the worksheet and what all different sections are there? What do we do everywhere? Okay, so first let's open the worksheet. Go to the sheet one over here. This will open the worksheet. This is called worksheet or sheet here as you can see. Okay. Now, what do we have here? There are many options. So, let's just call out these options. What do we have here? So, we have at the very top, we have the we have this menu section. This is called your menus or drop-own menus login dropown Then you have a pain here in the left hand section. This is called your pain. You have your pain and this is called your pain. Okay. pane. Within the pane, there are two types of pane. One is the data pane that we are looking at. Data pane would show you all the fields, all the fields that you have. If you have multiple tables, it will show all the fields for the multiple tables. And then you also have an analytics pane which is which helps in the visualization part. We'll cover the analytics pane later. We'll mostly deal with the data pane here. So, this is your pane section. Then you can see a this section with the different let's say first of all this section let's call it the canvas. This is your canvas similar to the canvas we have in PowerBI in the report view. So whatever that we build, whatever visuals we build, we can have one canvas. Everything will be built over here. Here remember in PowerBI we have a big canvas and within a canvas we can have multiple visuals. In the in Tableau per worksheet we can have only one visual. Okay. Remember one visual per sheet [clears throat] in Tableau. You can have only one visual per sheet. Okay, you cannot have more than that. Then you have your rows and column shelf over here. So whatever that you visualize there is the concept of rows and columns that will come into play. Whatever is in rows and columns that will come into play and this is called your rows in the column shelf. Row column shelf. Then you have your cards which will help you build the visual. This is called your card pane where you have this marks written. This is called card or marks card. Marks card. This helps you in creating your visualizations, customizing your visualizations. So different types of markings that you can use. You can use different coloring options, size, details, text, all those which we are going to learn in detail how we can help those. Then you have some other shelf. You have a filter shelf. Filter shelf. So any filters you need to apply to your visual, this is applied through filter shelf. There is a page shelf. Okay. So if you want to apply any kind of navigation that you can apply through page shelf, we will cover this as well. Page shelf. Okay. Then at the bottom you can see this sheet right. So we can create new sheets. So at the bottom you'll see very small few icons at the bottom. Okay. Very small icons. So there are three icons over here. If you hover over on those icons, you will be able to see what they mean. Okay, if you hover over there is a new worksheet, new dashboard and new story. Okay, so just if you hover over you will be able to see. So there is a new sheet if you want to create a new sheet. New sheet. If you want to create a new dashboard and we'll cover what dashboard is. Okay, this is to create new dashboard. And finally, you have a new story. So if you want to create a new story, so that will be over here. We'll I'll tell you what is the difference between sheet, dashboard, story later on. But you can see all these and then here at the bottom you can have the data source. We have been seeing this. We can go back to the data source as we require. Okay. There are two more sections which we left which which are left over here. One is you can see a quick access toolbar. Quick access toolbar. So some quick actions that you can apply like sorting, labeling, pinning, uh changing the view mode. So there is this quick quick access toolbar. So you have some quick access tools over here in this quick access toolbar. And then you have this one more thing which is called the show me button. Okay, the show me actually if you expand this this will show you all the possible charts that are present in Tableau. So there are 24 these charts over here. 24 charts. You will be able to see those 24 charts over here in the show me panel. Show me panel lets you create the visuals easily. So it gives you some initially some help in creating the visualizations. Okay. So you can use show me panel just to create the visualizations a bit easily. So this is your show me panel. Okay. So these are all the different navigation options over here. Let me just take a screen grab. Right. So you have this worksheet or let me write interface. This is your worksheet interface. Okay. Now let's go back and understand the data a bit more. Okay. So now here when it comes to the data part there are these uh two categories in which your data is. If you see here in the tables under the table, you are able to see all the fields over here, right? All the fields. Okay. When you go to these fields, you will be able to see two types of fields here. One is your categorical fields. Categorical fields and other is your measures. Okay. So there are this concept of dimensions and measures. Okay. So, dimensions and measures. So, here if you see you have the all these fields and you will see that there are this some of these fields and after the fields you will see that there is a very dim gray line between some of the fields. Do you see that gray line? After some field there will be this gray line. Right? You can see that gray line. That gray line basically indicates which of the fields needs to be treated as the dimensions and which fields needs to be treated as measures. Okay. Okay. Or in short, which fields needs to be shown in the form of aggregated result and which fields needs to be shown in the don't summarized form as we saw in PowerBI. Okay. So anything that remains as the dimension at the top. Okay. Those will be displayed without doing any aggregation. measures. The nature of measure is that measure should always be shown with some kind of aggregation. It will be sum of values or count of values or u minimum of something, maximum or average of something. So measures so everything that is below this gray line is going to be shown in the form of measures which means aggregated result. Okay, it will always show the aggregated result. Everything which is above this gray line will show non-aggregated result which cannot be summarized, which cannot be some like the category name, city name, country name, all of those. Now if we look at the data, if we look at the data and if we see the let's say let's say this zip code field zip code field although this zip code field has the numeric data right some numbers are there I can I can choose it to be a numeric field as well. Although this is selected as a geographical field. But if I change it to the numeric field. Let's say I keep it in the numeric field. Then should I keep them as the dimension or in the measure section? Should it be in the dimension section or the measure section? Would you be taking the sum of all the zip code values or the postal code values? Right? You will not do it. Even any data which is numeric can still be considered as a dimension not as a measure. Okay? So you may have non- numeric data. Numeric data as the dimension too. Another example of this is a row ID. Right? Row ID. This is a numeric data. If you see this, this is a numeric data. row ID right 1 2 3 4 5 6 this is a whole number yet this is coming in the dimension because I don't want to aggregate it now is it possible if I want to change the natural behavior of this so whenever when when I'm going to visualize this this aggregations this kind of things would come into play that automatically results will be shown in the aggregated form or non-aggregated form those will come in Okay. But if I want to change any of the fields from dimension to measure, measure to dimension, I can do so. I can just simply drag the fields and drop it in the measure sections. You see as I drag it and if I go below or uh if I cross cross that gray line, I get this over values whether you want to keep them under dimensions or under measures. So if I put row ID into measures, drag and drop it over here, it will be dragged and dropped. It will be present. But when I'm going to use it in any of the um visual, let's say if I use it in this visual, it'll show the sum of the value. Okay, it will show the sum of the value automatically because now I have converted this into a measure which is not its natural behavior. That is not how it should be used. So let me delete it. Okay, I don't I didn't want to go to this point right now to show the value but I wanted to explain this. So row ID it should be a dimension. So I'll once again drag and put it under the dimension section. Okay. So data data will be of two categories here broadly. So there is dimensions and then there there are measures. Dimensions are categorical fields, okay? Or non-aggregated which should not be aggregated. There should be a individual value needs to be displayed. Okay. While measures are measures are aggregated quantitative fields. Okay. This will be fields to display aggregated values. Okay. Any fields which is to be used to display aggregated values that will be a measure. Okay. Okay. This part is clear. What is a dimension and what is a measure? So your data would be divided into two parts. Dimensions and measures. Okay. Now let's now let's understand how we can create any visual over here. So here you have this um okay before we go into that as well there is also this colors which indicate what is a dimension and what is a uh what is a measure okay so if you see whenever I'm selecting any fields over here you see the color of that is appearing over here so here if you see if I'm selecting this this is all coming in the blue color. Correct? If I select any of the fields over here, see the color of this. These are all coming in the green color. Okay? So, this is also a distinction that you can identify whether you are selecting a dimension or whether you are selecting a measure. Okay. Blue will be dimensions, green will be measures. Green pills. Sorry, blue pills. Green pills. Why I'm calling it pills? It looks like a pill, right? The shape looks like a pill. So blue pill is your dimension and green pill are your measures. Okay. To visualize data you have to use these shelves. Okay. You have to use these shelves. So we have the column shelf and we have the row shelf over here. Okay. Now this row and column this basically this tells you how the data will populate once you select the fields in the row or the column shelf. So as next to this column and rows you can see this lines. So in the column you see these vertical three lines and in the rows you see the horizontal three lines. Right? You are able to see those lines. these lines everyone. Okay. So what this these lines indicate horizontal and uh vertical is that whenever you are going to add anything in the column shelf right data will be read like this. columns are created. When you have the columns data will be read like this um in this way vertical any field that you put in the column they will be appearing like this but like I'm giving you a bar example but they let's say if I put the region in the columns they will come in separate region in this column wise okay anything that you keep in the those rows that will appear in the row wise. So if you keep anything in the row shelf, this will appear like this. You will have the data coming in this order rows wise. Okay, let me show that to you. Once we select fields, it'll be clearer. Let's say I want to show the sales by category. Okay, I want to show sales by category. Okay, so what I will do in sales by category, I want to see my categories in the columns or let me let me keep my categories in the rows. Let's let's do it that way. So what I'm going to do categories in rows category drag and drop it in the row shelf. Okay, you see categories as soon as you added in the row shelf your values are appearing in the row-wise order. Okay, if you had selected this in the column, the values would have appeared in separate columns. So each of the category is coming in one column. When you select rows, these are coming in the rows. Okay. Now I have just added the category name and just against this this ABC is coming. So this ABC does not mean anything. It is an indication that I'm trying to create a table over here. This ABC does not represent anything else. It's just that I'm trying to create a table. But I want to show sales by category. Sales by category. So pick the sales measure and drag and drop it onto the columns shelf. Okay. Can you do all of you do that and tell me if you are able to see the visual like this? Now here I am able to see these three categories and their sales values. Now currently this is showing very yes niha those are x-axis y axis those are symbol for x ais y axis similar to that now currently I am able to see my data in this view right now I want to see this table showing in my in the entire view I want to use the entire view of my sheet entire canvas to view this so at the top in the standard selection view mode. Change the view mode to entire view. Switch it to entire view and you'll be able to see the visual covering the full view. Okay. Now at the moment I'm able to see the sales for each of the category. But wouldn't it be easier if I keep my data sorted. Wouldn't it be easier for me to understand which is the highest? Although there are only three categories. So I can say that quite easily that technology has the highest sales then furniture and then office goods. But if this data were in sorted order, it would be even better, right? So we can always sort this. And in the quick access toolbar, you have this sorting shortcuts over here. So this first one will sort it in the descend ascending order. We want it to be sorted in the descending order. So the highest value at the top and then lowest at the bottom. So select this option and view the data in the sorted order. Okay. So click on this button. Sort order. Now I also want to see the values on these bars. I want to also see the data labels. Okay. So it would be easier to to know the exact sales values if I know this data labels to appear. So if you click on this part there is this quick access option of turning the data label. Click on this and see whether you are able to see the value of the sales on the bar. Okay. So we can turn on the data label. Okay. Now these are the some standard things that you should always do whenever you are visualizing anything which is adding the data labels. Secondly sorting the results. Whenever you are showing any kind of data always show the data in the sorted manner. Okay. Always give these data labels wherever feasible. That will help you view the numbers easily, compare the data easily. And finally, you should always give title to your visual. Whenever there is a visual, you always give a title. So for giving the visual title, you have this title actually at the top. This sheet one, this is the title. So what happens in Tableau is whatever title that you give to your sheet at the bottom that title automatically becomes your chart title too. Okay. So if I go here at the bottom in the sheet I rename it to let's say sales [snorts] by category. Okay. Automatically at the top my title also changes to sales by category. If I want to further format my title, I want to make it bold, I want to change the color of this, then you doubleclick on this title and you get this addit edit title option. So currently this is coming as a parameter. So there is a parameter of sheet name. So it will by default sheet name parameter is taken as the name of the title. That's why automatically the sheet name is coming. If you want to increase the font size, if you want to change make it bold, if you want to change the color of this, you can do all these kind of formatting in the edit title. Okay, I'll leave it to this the moment. And let's create another sheet. Okay, so let's create another sheet. And this time, just click on the new sheet. It'll create open a new sheet over here. Click on new worksheet. You'll get sheet two. Okay, in this sheet I want to analyze my data over time. Okay, I want to analyze my data over time. So I want to see what my monthly sales are looking like. Okay, what my monthly sales are looking like. So when it comes to the monthly sales you want to look at now at the moment you have order date, this is one date field. Now you don't need to create a separate field for year or separate field for month that is not required. Just you can always use the date field and with the date fields you can simply you can convert them to be shown to be converted into year, month, quarter all those things. So simply select order date and drag it onto the column shelf. Drag and drop it on the column shelf. Okay, so by default this dates are aggregated at the year level. Okay, dates are aggregated at the year level. Okay, now if I want to see sales by this, I can drag sales into the rows and I get data like this. This chart automatically becomes based on the automatic design of Tableau. We can always change the chart type if you want to see some other visual. Okay, this chart is nothing but or this line or this is nothing but this is called your mark. So whatever visual that you see what marking of that visual would be. So here the marking is if you see here marks and this is the automatic this is converted to line. If I want it to be changed to a bar I can go to the mark and I can change it to bar. It will show the marking in the form of bar. If I want to change it to something else let's say some uh area circle text I can do all these different types of marking. Right? area I can make it area but let it be the automatic line chart that is coming right now okay so this is coming in the line now if I need to understand the insight from this chart what can I say so from the previous sales by category we can see that technology is the highest selling category but if we look at the sales over time we can make an inference that the 2016 and 2017 having the highest sales. Your sales are increasing right now in this year like currently this data is shown in the year level and we have this hierarchy automatically coming here as well. If I click on this plus button it will then break my data down into within each year I'm getting the data by each quarter. So if I click on this plus icon, I go to the quarter level and now I can compare each quarter of each year. Okay. Now I can compare each quarter of each year. So I'm able to see year- wise as well as quarter- wise. If I click on plus again, I will be able to compare one more detail. It will be quarter and within that quarter all the months. But when I have data like this, it becomes too difficult to analyze because of these broken lines. Okay, this is coming in the broken line. So what we will do here is one is that we can choose at what level we want to see the dates be it at the year level, quarter level or month month level and we can choose whether we want to show them as the continuous value. So what I'll do here is I will remove these. Go and remove go and remove this year and the order date that we have. If I go to this small drop-down, I can choose at what level I want to show my data. Okay. So I want to show my data. Here you have these different types of date parts in which you want to you can show like years or quarters or months a day. So there are these two sections over here. One is your discrete section. Discrete section means if I choose quarters wise, let's say I want to show the data by quarter. If I choose this upper section then it'll just show discreetly how many quarters do I have in my data. Okay. What does it mean? Discrete means that whatever unique quarters that I have in the dates those will be shown or the distinct quarters that I have those will be shown. Now you can have any amount of historical data. There are only four quarters right? Q1, Q2, Q3, Q4. The years would be different for those quarters. But if we just consider quarters, they will always be four quarters. So if we go with this discrete option, it will show combine all quarters of all the years into like each quarter of all the years into that quarter. So Q2 of all the years will be combined together. Q1 of all the years will be combined together. So if I want to see some seasonality based analysis like okay I want to see over the years how my Q1 has performed versus Q2 versus Q3 Q4 that you can do but normally you don't do it like this you don't make it discrete but you want to see them as continuous. So below this line you can still have this year, quarter, month and these options but these will be continuous. So if I select quarter over here in this it will show the continuous values means it will show for each year all the quarters. Okay. So 21 uh 2014 quarter 1 quarter 2 quarter 3 and all the years are being shown here. Okay. So you have this discrete and you have this continuous. Okay. Also remember when you show the data in continuous then this field also this turns green. So measures as well as the continuous data are shown in the green pill discrete. If I had this discrete this will be coming in the blue. So dimensions or the discrete values will be in blue. Continuous will be in green. Okay. Now what is your inference from this? Okay. 2017 Q4 has the highest sales. Okay. There is a pattern. There is a trend that you can see. Whenever we analyze data over time over dates we always look at some trend whether the data is following some trend some seasonality some patterns are there. So Q4 always becomes your highest in all the years across all the quarters Q4 is where the sales become highest. So in this business there would be now you if you are if you know which uh data you are analyzing which type of business they are into what kind of um um sales channels do they have. If you know some more information about those then you will be able to correlate this more. For example Q4 has couple of things. There are festivals right there are there is festivals like you'll have um Christmas right you will have Christmas then in the US you will have the black Friday cyber Monday sales right you will have Thanksgiving yeah in other parts as well other uh uh places as well you will have like in India you have Diwali correct so you will have different types of seasonal periods that comes in quarterfall there would be more promotions there will be more discounts right so the sales is likely to increase that is there overall also you can see that there is an increasing trend in general even if you look that the trend if you see this is going in the upward direction only. This is not going in the downward. This is going in the upward direction. So overall trend is positive. Okay. There is seasonality but overall trend is looking positive. Okay. Also there is a continuous dip after Q4. You can see that there is a dip everywhere. There is a sharp dip after Q4. So the next year sales always starts with a dip. Now there can be multiple questions that you can ask why this dip is coming. Is there um a reason for it? So that is a question to the business why from Q4 of course there will be more promotion, more sales that is there but the dip is quite big. dip is even going like it is going below your Q4 Q3 sales as well for the previous year. So all the next year's Q1 is lesser than Q3 and Q4 of the last year. So this is how you can do a of course there will be other things that you can see but this trend analysis this is always helpful and you can answer multiple questions over here related to the sales trend and seasonality. Okay. Now let's look at one more visual over here and I'll keep it. Let me go to one more visual and here I'm going to make a I'm going to use a map visual over here. Okay, I'm going to use a map visual. So for the map visual what you can do is you can go to select location field. Okay. So you'll be using this marks card fields. So drag the country field. Country field is a location. The data type is also geographic. If you put it uh and add it to the details card. So just drag and drop it on top of the details card. Okay. Can you do that? And automatically does the map visual appear for you. Drag country. Drag. Drag it. Drop it on the details card. Do you see the map? Okay. Now I'm able to see just one point over here for United States because all we have is United States data. Okay. So we have added country into the details. Now I want to see the data by states. So you also have states data over here. Add states as well into the detail. Okay. So now we can see different states where the sales have been and we can see which all states it is coming from. But I want to know that from which state the sales are more which states the sales are less. Okay. So all these if you see the mark over here this is a circle right? This is a circle or it is called a bubble. This is also called a bubble. So automatically these are coming in the bubble form right this is a mark type which is coming as bubble over here but I want this bubble size to be with respect to the amount of sales that I'm getting from these states. So the next that you need to do here is you can simply bring sales and drag and drop it on the size card. Okay. Size card. Drag and drop it on top of the size card. Okay. Does the view change now instead of seeing all those different bubbles. Now you see only a few. Some are very small, some are bigger. Okay. Now this bubble size, let's say I want to increase this bubble size a bit so that I'm able to see more bubbles over here. So go to the size, click on the size and just expand. Make this size a bit bigger so that you are able to see more bubbles. So one, I increase the size of this bubble bubbles everywhere. Okay. Secondly, I'm going to also change some property of these bubbles to make it more visible. So for example, the border of this bubble is in some other color. So I'll just fix that and you tell me whether that would be helpful in viewing the data better. So right now we are looking at it like this. If I change this sum of sales that I see over here in the bubble and if I go to okay it'll be in the colors actually will be in the colors. Sorry it'll be in the colors because we are looking at these colors over here right. So go to the colors click on the colors. Okay. And here I want to make just one minor some minor changes. This hello effect I'll just remove the hello effect. Hello just make it to none. Okay now these hellos are gone. And secondly the border that is coming. Let me make the border as none too. Okay let me make the border as none too. Okay. Now or let me do this border. I'll put a slight color over here. Border. Let's let's put a dark gray color so that these bubbles are becoming more evident. You're able to see the bubbles more bubbles rather than earlier. Okay. So now we are able to see more bubbles. Okay. What? What did I do in the colors? I went and changed this effects. Hello. I changed it to earlier it was automatic. I changed the automatic to none. No hello. And border I gave uh this dark black border. That's all. Okay. So now I'm able to see which country I'm getting the highest sales. Can you tell me where I'm getting the highest sales from? Okay, it's California. Okay, so statewise I know that most of the sales is coming from the California. Now I can see that. But when it comes to the region now I can see that my >> region wise >> my region are more into the >> east region having more sales than the west. >> Okay. East region is having the more sales. We have more bubbles over here as compared to the west. Okay, another thing that I can do here is now I can see the size of these bubbles based on the sales. But I can also see out of these uh sales which of the sales are more profitable. Okay. So now drag the profit and drop it onto the color mark. Color mark. Okay. And now see the difference. And now tell me what does now this tell you there are many states where you can still see high sales but they may not be profitable. Okay. So when it when I added this profit into the color a color gradient got added. If you see at the right this is showing the color gradient which in which it is showing. So anything which is coming in this yellowish and reddish color that is indicating losses while the blue ones are indicating the gains. So there are California is also profitable as well as high sales. New New York is also profitable and high sales. But there are other countries and other states like Texas where sales is high but it's not profitable. Right? So Texas, Florida, Pennsylvania, Ohio, these are high sales but less profit or actually loss. Okay. So, we keeping we are going to keep our learning to this much today. Uh we'll continue more on the visualizations tomorrow. Let's just rename this to sales and profit by states. Okay, we got this. And now the last thing that you all should do is save your file. So always save your workbook. So go to file under file you have this option of go to save okay file and save don't save it to tableau public because that will publish it online but just save it locally. Go to save and here you can just go and save it as let's say this is your in the save as type you can use uh for going forward just save it as tableau packaged workbook TWWBX select that and save it okay so let's start with this quick recap uh yesterday we learned about the basics of data visualization ation and uh the different roles that exist in the BI projects, right? Uh we learned about Tableau, the different products of Tableau, Tableau Desktop, Tableau server, Tableau cloud and Tableau public. And we started using Tableau public. We learned about the interface in the data source, data source interface. We saw the worksheet interface. We saw how we can add the data source. how we can add the data and what are the uh properties in terms of just doing the initial data cleaning in terms of hiding unhiding the columns doing the datas column split all those and then we loaded the data we started analyzing the data. So we first created sales by category visual right. We created a sales trend which is a quarterly trend of the sales and we created a map visual which shows the sales by states and colored by profit. Okay. Now let's also create one more visual here. Okay. This time it will be This time it will be I want to see which region is having the highest sales. Okay. Which region is having the highest sales? Okay. So which region is having the highest sales? So here we have the regions. I can bring regions into the column. drag put it in the or rather let me put it in the column we have the region and I will put sales in the column okay so this will give me regional sales so in each region I'm getting the I can see uh total sales now the standard process that we have always to do so we will do the data labels So click on to add the data labels. Sort the data in the descending order. Make the view to entire view. Okay. Okay. Now this is done. Now I want to see this breakdown of the region by different segments. So I have product segments. I want to see the breakdown of the sales by segment. So what I can do here? I have this color mark under the mark color. I want to segment these. I want to break these down these regions by the segment. So I can bring segment drag and drop it on the color. And you'll see all these results are now segmented by divided by the different segments. So we have consumer, corporate and home office. I can see the details divided by segments. Okay, this is similar to how we can add legends in PowerBI in the visualizations. Similarly, you can do these colors and this will create a legend in your visual. Okay, what did we do here? Let me remove this. Remove. What did we do here? I wanted to see this region. Each region I want to see by different segments how the total sales are for each region. Okay, I want to do this. So for that purpose, I can simply drag segment. Okay, what do we need to do? Drag segment onto the onto the color shelf. Okay, onto the color shelf. You're not able to see the data labels. So we have the standard steps, right? We we learned yesterday that every time follow these steps. Always turn on the data label from here. Always sort the visuals from here. Always change the view to entire view until it's not required. You do these steps like 99% of the times. Segment bring into the color. Now you'll be able to see the different segments and also different colors for it. Okay. So nha to your question yes you can put uh region in details too but you are saying region into details or you are saying you put segment into details. You can put anything anywhere right? I'm trying to first I'm trying to build something meaningful here. and then we'll learn more about the visualizations how we should be using all these components. Okay. Now let's say I created these worksheets. Okay. I've created these four sheets and now I want to create a dashboard for the end users. Okay. I want to create a dashboard. So individually users can go to these sheets and see what kind of analysis we have done that's there. But I want to create a dashboard as well. One dashboard where I can see multiple visuals together. So in Tableau what happens is you first create the worksheets and then you use those worksheets to create dashboards. Okay. So let's create a new dashboard. So here in these small icons here you have the new worksheet. Next to that you have new dashboard. Next to that you have new dashboard. Click on new dashboard. Okay. If you click on new dashboard, a new dashboard page will pop up. Okay. Now in the dashboard page here there are these different pane over here. There is a different pane. You have some other options over here. Dashboard size sheet objects. Some different options are available over here. >> [snorts] >> So in the dashboard what we need to do is use these sheets to populate on the dashboard. Before we do any of that let us always work on the size of our dashboard. What should be my size of this dashboard? So you can imagine that this is your canvas. This is your canvas. And what should be the size of this? So currently this is on a smaller size. I will change this size to so how do we change this size? I'll change this size to a bigger size and an automatic size actually. So there is this size option over here. This is telling me what this size right now is. I want to modify this. So I want to go to this small drop-down over here. Okay. And here in the size options instead of using this range. So this is allowing me to keep the range minimum to maximum. Instead of this range, I keep this size to automatic. Okay, everyone do this. Go to size and choose the option automatic and tell me if it increases your canvas size to full width. With automatic size, it will adjust your size would adjust based on your screen size. Okay? Whatever device screen size that you are having, it will adjust according to that. So, we'll use that. Okay. We'll go and choose the automatic. Okay. Now, we have got it over here. Now, I need to just add my visualizations over here. So, you have these sheets, worksheets over here that we can see. All you need to do is drag and drop your worksheets to start coming on the appearing on the dashboard. So drag drop it sales by category you have dragged it over here. It by default it occupies the entire space. Okay. So first visual you have added it occupies the whole space. Then sales by region. I want to keep this next to sales by country. So you drag and now you see don't leave the mouse. Just keep dragging it and keep it. As soon as you go any part of your dashboard, it highlights that part. So it kind of adjusts where I want to put my this worksheet. So automatically it will occupy the space whether I want to use the left half of the of the uh dashboard, right half of the dashboard, bottom of the dashboard or top of the dashboard. Okay, let's say I use this top of the dashboard over here and dragged it and dropped it. So here automatically when you when you when you put visuals from worksheets to the dashboard it readjusts accordingly. All you need to do is drag and drop. It will readjust accordingly. It will just occupy equal space. Okay. It'll occupy that space. So now I can see sales by region coming at the top and sales by category coming at the bottom. Now I want to see the sales trend over here. Okay. So sales trend. Now let's say I put sales trend. Drag and I'll put it at the bottom here. I'll put sales trend. Okay. So I've got these three and the sales by profit and region the map visual I'll drag and put it in the right half. Okay. So go where it is the right half is visible whenever that is highlighted. Now you can drop it and it will occupy that space in the right half section. Okay. So you got all your visuals over here. Now you can resize whichever you want to resize. I can maybe I can make this a bit smaller. Okay. Sales by region. I can make sales by category smaller. Sales trend like this. And the region visual like this. Okay. So create your visuals dashboard like this. Pria it does not come like this. It will come as per how you will place your visuals. So you can always you can always you have this. It looks like all this visual and you click on this. You see it looks like a briefcase. Okay. It looks like a briefcase briefcase with a handle at the top. Right? So if you want to drag and move around the visual just just hold it by the hand handle and drag it and place it anywhere. So eventually place it as how it appears on my screen. See if you are able to place it. How you will be able to place it? Once you start placing it, it will highlight that area in gray color and it will allow you to choose and show it in that. Once it appears in the gray color, you can do that. Okay. Okay, that's fine. What you can do is um the other visuals which are coming at the bottom, you can drag them and put them under the first visual sales by region and then it'll allow you to adjust your visuals. This is this is like uh this once you start using it once you start dragging these uh options around you will get much more familiar with it. Okay. So how you can do it even better is let's say I remove this. Okay. I can just delete remove from the dashboard. Okay. Remove from the dashboard. Remove from the dashboard. and remove. Okay, you can bring the sales by region first. So, you can bring sales by region first. Sorry, uh not sales by region but the map visual. Sales and profit map first. Okay, you got the map visual first. Then you can put sales by region in the left. Then below that you can put the sales by category just below that. Right? Just drag and put it below that. you'll get this and then sales trend below this. So if this is easier for you to do, you can do it in this way too. Either way, whichever way you are able to do it easy. So you can divide it into two parts first and then put in vertical order. Okay. Now here here let's say we should always try to keep as less clutter on the dashboard as much as possible. Now if I'm if I am using these labels over here on the visual then I may not require these excess labels right this may be just additional information. If my title says sales by region, then I may not even need to put this access title with sales. Do you think it's necessary that I need to put this excess title? Do you think it is necessary or can we remove that? We have given the title that we are analyzing sales. Then do we need this excess title? Right, this is not necessary. We don't really need the access title. So what you can do directly over here in the visual itself you can go to this access click on this the access will get highlighted. You can right click on this and you can go to you can just deselect the show header. Okay this will remove the this will remove the entire access. But let me instead let me first show you that I want to just remove the title. So edit access. Okay. Edit access. And you want to access titles. You can also remove the title from here. Just remove the title. Don't keep any title. So this will just have the access labels but not the title. If you want to keep the label, you can do that too. If you want to remove the label as well, you can do so. So I'll go ahead and remove this entire header part because I know that there is I can see this information directly over here. Similarly I can get rid of this two show header. In this one we know that we are showing by quarter only over here. All right. So I can remove this title but I would still want to see these labels. So I can go and go to access title and I can just remove this title over here. Okay, now it becomes slightly cleaner. You can also make changes in the size of the fonts which we'll come to. Let's say these titles, these are a bit bigger, right? The titles are a bit bigger. You can double click on this title at the top. Double click the title at the top. Select this sheet and reduce the font size from 15 to 12. Okay. Everywhere double click. Select the entire sheet. Change it to 12. Okay. It becomes it became a bit more clearer now. Correct. So it would also be better if we just currently this looks like this. Right? You have all these visuals. So just it would be better if you just also add the boundaries around these visuals. That will make it a bit cleaner and segmented. So just select any of the visual. Next to the dashboard there is layout option. Go to layout. Okay. Go to layout and here you can choose the border and choose border as instead of none you can choose a solid line. Okay. And see how it looks like. And for all of these, just choose the border as a solid line. Now it looks more segmented better. Now if you see that when we added these worksheets, along with these worksheets, the legends were also added to the dashboard. You can see the legends in the right hand corner over here. Correct? The legends are added over here. Now these legend belong to specific visuals. Right? For example, this legend of the size of the bubble, this belongs to the map visual. This color coding of profit, this also belongs to this visual. These colors for the segment, this belongs to this visual. Right. So when you have dashboards where you have multiple legends, it might sometimes might be confusing which le which which color code should we refer for which visual, right? Or which se which legend is actually for which visual. So we should also be seeing how we can keep these segments. These segments also appear like visuals over here. Okay. So one way is that you can go ahead and drag and drop them like handles right. So you can drag and drop them on the top on the bottom like this. Okay. So segment you can keep it let's say over here you can keep it like this. Colors you can drag it. size you can drag and drop them over here like this next to the map. Okay. Now this this size although this is indicating that which bubble size is corresponding to which type of value but actually this is not much valuable. You would seldom look at these size brackets like this and see you would be able to get the idea with these bubbles. So actually it would not be a bad idea to even drop this from the dashboard. This part. Okay, let me actually drop this remove it. Just go and remove it from the dashboard and just leave this part. Okay. Now what happens here with the these legends? Okay. These are also kind of visuals when you start dragging them they will occupy the space where you place it. So basically the way these default setting is for all these visuals is that they appear as tiles individual tiles and the tiles do not overlap. Tiles do not overlap. So if you have seen creating visuals in PowerBI, PowerBI everything can overlap on top of each other, right? Two visuals can overlap on top of each other. The default setting is like that. In Tableau, the default setting is that everything would be tiled. They would not overlap. If even if I try to drag and drop it on top of it, it will appear in this right hand side or the left hand side or the bottom. It will be tiled. Okay. So I don't want these to occupy unnecessary space these legends. So I will for these legends I'll make them from tiled to floating objects. Okay. Floating objects. Floating object can float over another visual. So you see here a object and you have two options tiled and floating. Okay. Tiled and floating. So you can convert a tie an object to a floating object. Just select this make it a floating object. Okay. Okay. It will not get converted from here. You can actually go and make it from here. More options. Make it floating. Okay. Just make it floating. Okay. Floating. As soon as you convert it into floating, it can now overlap on top of the visuals. Now it would be fine if I just put it inside this map visual occupying some space. Okay, it will not harm anything. It will be within on the map visual. Okay, so just click on the visual. There is a small drop-down in the legend for more options. And from there you can make them floating float. Okay. And then you can drag and drop it anywhere on the visual that you want. I will make it like this. Make it smaller and wider for these segment names to be properly readable at the top inside the visual. Two legends as floating objects and added them inside the visuals. Serban are you able to do now? Just make them floating. Uh there is more option over here. convert that it to a floating object and then show okay business intelligence tool that I'm developing that means that I should be able to analyze the results so we are trying to analyze the results and we can see the sales by okay we can see the sales by region sales by category sales trend sales and profit on the map right now in PowerBI if you remember everything is by default interactive. Correct? Everything is interactive. If I click on this technology over here, it should it filters the other visuals. If I click on furniture, it filters the other visuals. So that happens automatically in PowerBI. In Tableau, it does not happen. So these are not all interactive, right? So here I want to make it such that I should be able to interact. So what I want here is I should be able to filter the other visuals based on one visual. So if I go to the sales by category, if I select this visual, you see some options over here. One of the option is this use as filter. Use as filter. So just enable this use as filter. If you enable this, this will become this is get highlighted. And now if I select any of these category, let's say if I selected technology, I'm able to see the trend for the technology sales of the technology over map the sales of technology products across the region across the consumer segments. So this works as a filter. I'm able to see filtered result in all the visuals. Okay. Same thing I would also want to do with this region and the segments. So on this visual as well you can make that as well use as a filter. Okay. Now this is also used as a filter. Now let's say I want to see the consumer segment in the west region. Okay. So in the consumer segment I can see the sales by technologies, sales by categories, trend where in the west region the sales are high, low all those information coming over here. Okay. So this is much more interactive now. Users can slice and dice view the results as they require wherever they want to focus on. Okay. So south is the for example south is the lowest selling region. So we can see in south specifically I can select multiple fields in south. You can select by clicking on control and select. So you can see the entire south region and see what is happening in the south. Where am I getting most sales which is the better one which is not. Okay. So mostly south does not look too good. Most of the these big ones are actually uh loss making states. Okay. So overall results for the south is not looking good. Even if you look at the sales trend this is also quite flat over the period. Just off late it has picked up a bit but it has been flat throughout most of the time. Okay. So we can analyze the data start analyzing as we require here. Okay. If you want to have much more just control view, you can go to this full screen presentation mode. Click on the presentation mode and it will appear on full screen. And now you don't see all these other disturbances, other uh settings, all those things. You just go into the analysis mode directly over here. Right? So you can go to full screen mode, presentation mode and you come back from the presentation mode from this right corner. So this is how we can create a dashboard. This is our first dashboard. All right. Now let's work a bit more on the data preparation side. Okay. data preparation side. So, so far we have just looked at the very high level data source settings some of the options then the fields and just creating some visuals to analyze the data and some standard practices in analyzing the data and creating a simple dashboard. Now let's understand a bit about the data preparation some options that you can do while preparing the data. So for that I want you to open a new new file. Okay, open a new file. So you can directly go from file and you can go to new file new. Okay, it will open a blank Tableau file a blank one. Okay. So now let us understand this with a bit more of example. So here let's say for data preparation I want to do a bit of let me go to just a second data preparation. So data preparation in Tableau. So as we learned that there is also a separate tool called tableau prep which which has much more features in terms of data cleaning data transformation but that's a separate tool we are not going to cover work on that here we are going to see directly in Tableau how we can do some data preparation and here when it comes to data preparation it is mostly related to how we can work with multiple tables and how we can use them how we combine them. Okay. So, similar to how we learned about combining the tables in Power Query, similarly we can also do the combining of the tables in Tableau. Okay. So, when it comes to combining, there are two ways in which you work on the combining data in Tableau. One is when your data is or your tables are coming from a single source. Okay. From a single connection from a single source. If you are getting multiple tables, okay, so there is okay. Combining data in combining data when tables from single source okay single data source. So when you are using single data source you can do joins okay you can do joins you can do union and you can do relationships. Okay there is joints there is union there is relationship. Okay. So this join union relationship you would also see this similar to how we have learned in PowerBI as well. So when it comes to joins then joins are similar to merge query. So joins are similar to that. Join means that you are you are basically joining the two tables and for joining you need to use a field to to use as a join. So there needs to be some level of detail at which the join happens. Okay, some level of detail at which the join happens. For example, there is a one table for the employee uh let's say the employee attendance, right? With having the details of employee ID wise information and then you have another table which contains employee salary information. Once again you have the employee ID and the details. So these two tables can be joined because there is a level of detail which is same which is employee ID in both the tables and with its can be used to make relationship or I should say joins. So join joins means you are physically bringing the columns from other table into one table. Okay that is join. Union on the other hand is union is like append. We learned about append query. So whenever you want to do row-wise addition of the data from multiple tables then you can use union. Union will combine multiple tables into a single big table. Okay. So you can do joins you can do unions. And then the third type of combining appears which is relationships. And relationships is you are making just just the relationship between the tables by using some field. And in the relationships you are not physically joining the tables. It's just logically the tables are related and you can use them when you start analyzing. You can use that relationship when you start analyzing. So when it comes to relationships you'll have the cardality those kind of things. So these are the types of combining that you can do when you have the tables coming from a single data source. Other type is when tables come from different data source. Okay. When tables come from different data source. When tables come from different data source then you cannot do do joins you cannot do uh relationships that is not feasible. In that case you use another feature in Tableau which is called data blending. Data blending. Okay. This is a very powerful feature and one of the most important feature in Tableau which makes it like unique data blending. So data blending can be performed even when the level of details in two uh tables are different. Okay, level of details are different or there is no relationship with them. But still you can use blending to combine two different data sources in when you are visualizing them. So at the time of visualization you can blend the data. Okay. And it lets you let's connect or not connect you combine the data on the fly while visualizing. Okay, we'll see the example of this. It will be more clearer on this. And in blending you may have two tables at two different level of detail. One in one one visual you may have data uh which is showing daily result in another table you have monthly but you can still combine those when visualizing it and it works. Okay. So we'll look into these with some examples. Okay. [snorts] So let's look at this. So first let's let's look at one of the examples. So the new workbook that you have created open. Okay. We're going to connect to we're going to connect to an example. Let me uh let me look for the example. [snorts] So there is a joints example. Are you all able to see the joints example file in the data? Right. Okay. So let us connect to that. So we'll go to source connect to joints example. Okay. Now look at this data. Here we have two sheets. One is customers other is orders. Okay. One is customers, other is order. So if you want to just look at what the content of these two tables are, you can see this small icon within the table view data. I can simply click on this and I can see a preview of the data. Okay. So customers I have this information. Customer, the city of the customer, customer name, customer ID. Okay. So I have four customers over here with unique ids, the city and the name. In orders table, let me close this. Similarly, orders table, you can view the data here. So we have the order information, order date, which customer ordered, how much they ordered, what is the order ID. So information like this, right? Now I can join these two tables to bring the customer name from the customer table, customer city from the customer table and join it in the orders table. Okay. So I want to know which customers have ordered. Okay. So what I will do here is first I will bring the orders table over here. Drag and drop orders over here. Okay. So we added the orders over here. Okay. Now I want to do the joining. So in the data source in the data um um this data source settings you have the data at two levels. You have the data at two levels. We looked at this level. So what you see here right now this is the logical layer. If you remember logical layer okay and then there is also a physical layer. Okay physical layer the joints happen in the physical layer. Joins and unions those happen in the physical layer while relationships happen in the logical layer. So we want to physically join these two tables. So we want to go to the physical layer. So what you need to do here you go okay and go to open okay click and open once again go here and open means you are going into the physical layer of this table okay you're going to the physical layer of this table so first you need to go to the physical layer and then whichever table that you want to join just drag and bring it next to this table. Bring don't bring it. If you bring drag and put it below this, it will automatically perform union. If you put it next to it, right to it, it will perform join. So, drag and place it right to it and it'll automatically do a join. It has performed a join over here. So, this is an inner join, right? Inner join means it will only keep the data which is common in both the tables and based on which if which field. So if you just click on this it'll tell you the details. So there is an inner join being performed over here with the first table orders table. We are using customer ID field equal to the customer ID field in the second table. Okay. So, customer ID field is being used to make the relationship to make the join. So, this is fine. Okay. Customer ID is the right field with which we should be joining. But here instead of inner [snorts] join, I would recommend using a left outer join because I want to keep all the orders information. I don't want to miss out on any of the orders if the customer ID is not present in the customer table. Right? I want to have all the orders if even if the customer ID table does not have full information. So I can go and choose left outer join. Okay, left outer join. And automatically this color code this symbol here is also changing to show that we are performing left outer join. Okay. And we are good. So left outer join is being performed over here. Output of the join can be seen here at the bottom once this left outer join is performed. So we can see the order ID the customer ID in the orders table 0 1 020 05. Okay. Now [clears throat] if you see in the customers table once again going to the view data in the customer's table we had customer ID 01 101 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 102 1 3 1 104 okay there is no 105. Okay. So in orders table we have orders from the customer 101 102 and 105. So there is one customer which is present in the orders table but it is not present in the customer table. So that's why when we did the left auto join it gave me rows for this 105 customer as null right the customer ID customer name customer city everything that is coming from the customers table is now coming as null okay with the left auto join if we had continued with the inner join it would have simply skipped that entry 105 would have been skipped because it is not present in the customer table. Okay. If we go with the right auto join then it will keep everything that is we that we have in the customers table right everything from the customers table all the customer 101 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 0 102 1 3 1 104 irrespective of whether we have the orders information or not for those and if we go with the full out of join then it'll keep all the records so all the orders information all the customer information wherever data is not available those all will be coming in null. Okay. If my intention is to have all the orders information and only the matching customer information, then I can continue with the left auto join as we discussed. Okay. So, we'll connect left auto join. Now, this joining is done. Okay. And I can close this. We now came back from the physical layer to back on the logical layer. And now in this orders table this is indicating that this there is some thing happened in the physical layer. So they see there are the two physical tables orders and customers which are being used here. If you want to see more details, you can double click on this even that will take you here in the physical layer and see what we had done here. Okay. So, you can do your joins in the physical layer like this. And now we have come to the this logical layer. So here now you can see let me show this once again what we did here. Double click on this. What we did let's say I want to remove this. We had the orders table. We first added the orders table. We double clicked. We came to the physical layer. Now I want to join it with the customer. So drag customer table next to the table. Just next write to it and it'll perform a join. Change the join type to left join because we want to have all the orders information and the matching customer information. Okay. So now join is being performed. We can close this and now you have this orders table. Now you will see the name of this orders table has changed to orders plus. If you see the name of this table has become orders plus. Okay. If you want to rename this table to something else. Let's say this is orders. Let's say uh you want to give some other name orders. Let's say I just gave some name here. Let's say I change it to orders full data. Right? Now it has all the customer information. Now if you go to this using this join data, if we go to sheet one, now you will see we are able to see both the tables and the columns coming from both of these tables. So what is coming from the customer table? What is coming from the orders table and we can analyze the data okay with this joint. So if I need to know how much order amount we got from each customer, I can bring customer into the column. I can bring order amount into the rows. I'm able to see that information. Okay. So there is one customer with without any information the 105 I can see that. So I can start visual analyzing this data with this join. Now let's take another example and this time we want to uh understand the union part. Very simple. Okay. Very simple. You can go and [snorts] create a new file. A new file. Open new one. Okay. New one. Go to the data source. And this time once again select the Excel connector and we will use the union example. Okay. Go to union example. Okay. Now here you have in the union example you have this data. Now imagine that you have the similar data but by different regions over here. So if you see the data over here. So this is region wise orders and sales. So east region orders sales. Similarly for other regions north region, north region orders and sales. Correct. Another region west region. South region we'll have this orders and sales. So we need to union this data. There is no need for these to be separate tables. These should be combined. So you can start with east table. Bring it over here. Okay. We are at the logical layer. Go to the physical layer because that's where you can do the junior uh union and joins. So double click or go to open. Okay. It'll take you to the physical layer. This is the physical layer and in the physical layer you can simply bring north and drag it below this. If you drag below this it will union. So now there are two tables which got added. If you see here at the bottom you got result of east region table and the north region table combined in the row-wise manner. If you add south as well, union that too it'll be added. More data is added here. Okay. If you add the west, you will get all these four regional data over here. Okay. So here we have got all the data. You can rename this to let's say instead of east you can call it regional. let's say regional sales data let's say I give this name right and now I can go to the sheet and I can start analyzing the data okay now here when you are when you are doing union you have noticed that there are new columns which are added which contains the sheet name and table name sheet name and table name so these metadata fields also becomes column fields here right these metadata like name of the table and name of the sheet they metadata also becomes column you see that you have this this is another property that happens that happens only in Tableau right so it also create a column with the table name and sheet with the table name now here we don't require both of these because we already have a column for the region name so the sheet name. Table name is not required. But whenever you want to just distinguish you have anything any uh table references to be used in visualization, you can keep these columns or you can choose to hide these columns. Okay. So we can go ahead. We can hide these columns if you require. And now we can we are all good analyzing this data region wise sales information. All right. So all of these data combined together and we can analyze. Okay. So this is how you can perform union. Very simple. Okay. Now let's learn about the third example which is how we can do the how we can use relationships. Okay. Relationships. So for relationships we'll go to um another example over here. This time we are going to use uh we are going to use the sample super store data. Okay, sample supertore data. Okay, sample supertore data. So another one you can open another one. Go to file. Go to new. Okay. And go to data source Excel file. and we'll go to sample super store data again. Okay. Now in sample supertore data we have orders, we have people, we have returns information. Now let's say I don't want to physically join these two tables. I can utilize the relationship between these two tables and with the relationship as well I can uh I can analyze the data I am able to visualize the data. So wherever it is feasible where you can use relationships you should always use relationships that is better in terms of performance. So let's see if we can use relationship here. So select orders first. Okay, this is the orders data. Okay, now I want to use along with orders I want to use uh people information. So people has information about who are the people manager in respective regions. Okay, so we have four region in our data and these are the regional managers in each of the region. I want to be able to analyze my data for these regional managers as well. Okay. So I can make a relationship. I don't need to physically join these two. I can make relationships. And relationships are added in the logical layer, not in the physical. So joins and unions are on the physical layer. But for relationships directly on the logical layer, you can drag and if you see this line would appear as soon as you bring the other table in the logical layer. Okay. So this line is indicating that there is relationship between these two orders and people. So if you hover over on this line it will tell you the relationship. So what is the relationship? Relationship at the moment is there is many to many relationship. Okay. And there is fields that is being used to make the relationship is region from the region table and region from the people table. Region from the orders table and region from the people table. Now I know that in the this table the people table I will have only one person per region. Okay I know that this is always going to be one person per region. This means that when we use region to make relationship with the orders table then I can make this relationship one to many. I don't need to keep it to many to many. So by default it is coming as many to many. In PowerBI it automatically looks at the tables. It automatically makes that decision whether the cardality is one to many or many to many. Here it will let you decide what will be the cardality in future as well. So it recommended that there will be many to many cardality but you if you think that it should be one to many or one to one then you can change that cardality here and where we can change this this is in this data model sorry this place where we can see the metadata and other information. Okay. So here we have some information some learning material how relationships differ from join. You can learn about this. Okay. So here the relationship is created with region field is equal to region. Okay. And in the performance options we can go and decide the cardality. So cardinality here should be many records from the orders table. Many records from the orders table would match with one record from the people table. So I can choose it to many to one. Many to one cardality I would want this way. Okay. And I can keep it to this cardality. I can choose what type of cardality it should be. Okay. Here in uh Tableau you can create relationships using more than one field two. So if there are more than one field which relates with two tables you can use those two. Okay. So you can add more fields and make the relationship stronger. Okay. So you can use multiple fields to make relationship and you need to specify the cardinality of it. Okay. So we have made relationship with the orders table, people table. This is many to one relationship. Okay. Now we also have this third table which is returns. returns as information. Yes. So person is manager. So you have the regional manager information and you want to know you want to use that in your analysis. You want to know the name of the person and by the name of the person you want to see the sales all those details. So that's why we are joining it with the region table. Okay. Now here we have the returns table too. Returns contains information about the orders which got returned. Okay, which got returned. Now here you can see the order ID and the returns. Okay. So we know which orders were returned in this and I want to make a relationship with this as well with the orders table because I want to know from the orders which were returned what is the detail about those orders which product were there what kind of uh location were those ordered in. So all those details those are present in the orders table. So I'll make relationship with the returns with the orders table using the order id. Okay. Okay. So once again bring return and this time make another join with the orders table here. Okay. Now orders table is linked to the people table. We specified the cardality. We we checked the relationship. Now you can go to the returns and you can see this is currently being related with the order ID. There is only one column with which you can make the relationship. So this is fine. So whenever the order id is equal to this then you use the relationship. Okay. Now here as well in the returns table you will have just one value yes or no right whether it is returned or not. So there will be only one row per order right. So even here in the returns table you can use the cardality many to one. Okay. Many to one cardality can be used here too. Okay. And now you can use this relationship to analyze the data. Okay. Now if we go to the sheet, if we go to the sheet, I am able to see all of these tables. So there is orders table at the top then you have people table and then you have returns table. Okay. Now with the relationship I can analyze the data. So if I need to know by the region manager let's say the person if I want to see how much sales were those from this person I can bring person from the people table and sales from the orders table and see the result. So for each of the regional manager this is how much sales was generated. So we are using the relationships now to analyze the data. Similarly if I want to analyze the data for returns which orders were returned or how many orders were returned. Okay. So I have that information too. So you have this return. Okay. And there is also one field which automatically gets created in Tableau which is the count of rows. So if you see for all of these table there is one one measure which is just the total count total count of rows. So I got this field as well. So if I need to know how many orders were returned from each uh category. Okay, how many orders are returned? If I need to know that I can do that very easily. I can bring category into the row or let's say in the column and I can bring the return count into the rows. So I'll know that okay most of my returns are from the office supplies. Okay, most of the returns are from the office supplies category. Okay. So we can use these fields coming from the relationships directly in the visualization. Normally like if you you are asking for an example of operators normally you use only the equal operator. In most of the scenarios you will use equal operator. You don't use the other operator. But this is an these are options which are available. Okay. These are options which are available. Let's say you want to make a relationship where the um uh if you are making any relationship it should look for matching the relationship from one table where the values are lesser than this. For example, you have one table having the daily data, another table having the monthly data, then you would know that the daily results would always be lesser than the monthly results. So you can make a relationship based on the sales value in the daily table will always be lesser than the sales in the monthly table. Right? [clears throat] Those kind of relationships you can make. But those are all weak relationships. Those are all weak relationships. So always look for making stronger relationships. Okay. This means you have not unioned all the tables. You have not unioned all the tables. So in this data preview, are you able to see all the four tables unioned? Here you can see the data preview all the data for all the regions. You may have not unioned the other tables. So similar to how you union north, union the west and the south too. Let us look another example which is when we use data from two different sources. When you have two different sources then how do we handle that? And that is actually done through data blending. Okay. Data blending. So let's open another file. Okay. Open a new one. Open a new one. Go to the data source. And in this one, first we'll connect to the product sales table. Okay, go open the product sales table. Okay, product sales. If you want to see the preview of this, you have data like this. So each product, name of the product, region, sales, and month of the sales. Now suppose this company for which you are analyzing the data they're into two different line of business altogether. One is in the electronic products, right? In the electronics product and another is on the uh some other non- electronic let's say some non- electronic let's say uh let's take example of some uh uh food products. Let's take that example. Food products completely different. One is electronic goods, another is food related products. Now these two are two different categories all together of the business. You will not be tracking the business having the electronic sales along with the food product. There will be separate stores. There will be separate uh data points with those will be connected. There will be separate tables from which those are coming. But if you want to just see these two businesses performance of these two businesses together in one dashboard, you can do that using data blending. So one data will have this electronics data. Then there can be one another data which will be having these product names would be related to some food related products. Okay. Product ID would be completely different. Everything would be different. Product names would be different, right? Different types of products. It's just that I will I want to know over periods over months whether the what kind of uh sales both of these businesses did. Okay. So you have this. Now uh let me see what example do I have. So first of all you can go to the sheet. Okay. And you can see the product sales over here. This data is appearing over here. Now I want to have one more data added over here. So what I will do here there is this option of add new data source here. I want to add a new data source over here. So click on this add new data source. directly a window will appear to add a new data source. Okay. And once again go and this time okay I have the regional targets information. Let's say I have the regional target. This is another example. Let's look at this regional target. What is this data? Okay. Now a separate data source is being added. You see a new window has appeared. You now have this regional targets. What kind of information do I have? This is another example. So here we just have the region all the different regions and the target information. What is the total target? Correct? So you have the regional target over here, monthly target. Okay. Now this data we are not going to do any changes. We'll just once again go to the sheet one. Now in the sheet one now you can see these two connections that we have two data sources product sales and regional target. You can choose which one you want to analyze when you can analyze these separately. If you click on product sales you see all the tables data related to product sales. If you use regional sales, you will have the data related to region, region wise sales information, right? Those data you have available. Now let's say I start my analysis and I went to the product sales and I want and I want to look at what my region wise. Let's say I drag region over here and I want to see what my region wise sales monthly sales is. Okay, I got this information region wise what is my total sales and from the other table if you go here in the other table as soon as you now go when I made any selection from the first table okay first of all I would want all of you to do till this part let me know if you are all here till here let me give me a confirm information if you are all able to see what I'm seeing here same information right yes I opened a new power uh new Tableau public new Tableau workbook okay now I have selected did data from the first table. Now if I need to bring data from the second table and it should show the related information then if I go to the second table I can see that there is this linking option is appearing. This kind of a link clock is appearing over here. Okay. What it does is it gives you an indication whether you want to blend these two tables using the region field. Okay. If you click on this okay then these would be related. If you don't want to click it, it will not be. So it says that the relationship there is a blending which is available through the region field. Okay. Now let's say if I bring monthly target into this visual along with the sum of sales and I put monthly target in the column next to this. Okay. It will show the related information. So the region field is actually coming from the first table. The monthly target is coming from the second table. But it is showing the correctly monthly target for each region. And that is because it has this it created this blending. Okay. Now you can see that there is these two tables have this blue and the red tick. This is indication that these two tables are using blending feature [snorts] and the data is blended through the region field. Okay, through the region field. If I remove this blending link, see all the results would be same. So it will not do any kind of filtering for the monthly target for each region. Right? This will not do. But if I leverage this blending feature, it will give me results filtered for that. So what this is doing is without you joining two tables or making relationship between two tables. It does not matter what kind of data first table has, second table has if there are two fields where the relationships could be created and it does not matter if the cardality is one to many, many to one whatever. If the for that visualization if this would work then you can use the blending for example we are aggregating the total sales at the region level right so for that aggregated result this blending would work it will give you the correct visualization it's very powerful feature only available in Tableau okay You can bring two data from two different businesses and you can analyze the data by based on one of the field which is blended. There are lot of examples. Now one of few of the examples I gave you two different businesses and you want to analyze the data for two different tables. Let's say you have the sales information for um uh electronic business, sales information from the food business. You can compare those two side by side just using the month fields which will be present in both of them. So you will just use the relationship on that make the blending based on that and you analyze the data. Secondly, blending is visual specific. visual specific if there are multiple depending on the type of visual you choose. If there is uh different field you want to blend the data for in one sheet you can use that column to blend. If there is another column to blend in another visual you can use that. Okay. So you can do you can do all of that. Okay. So in each of the sheets you can use different fields to make this blending and you get this output as per the blending is working. Okay. For example, if you have one table which is having product wise sales. Now you have the product name, you have the month name, you have the sales. Okay. Now in another table you will have information like uh let's say the product wise, month wise, region wise, you have budget information. Right now if you want to aggregate the data over the just the product level if you want to show the summary you can use the relationship just based on the product and you can you can make sorry the blending based on the product and you can view the result in one tape in another sheet you can use the blending based on the months and you can show the results by months. So there is all this can be done for individual worksheet. If you go to the new worksheet once again you can use a new blending option and you can visualize the data based on the new blending option. Okay. Another thing is that let's say you have one table having the day level information. Day level information, right? another and the second table is having the month level information. Now day and month you cannot make any kind of joins with those. It would not be possible. You cannot make any relationship with those. But just based on the blending you can do so. So you can blend those and you can date you can show the results at the month level when you choose the date. Right? Like we saw in the first this example right in the sales trend I chose this relationship to be at the sorry the visual at the quarter level. You can show this date at the month level and the other result which is already at the month level you can use that. Okay. So blending is much more versatile. It does not matter what kind of uh level of details two tables have. As long as the visualization requires that aggregation level and there is a field with which you can blend those table two tables, you can use blending. Okay. And it's quite like commonly used when Tableau is required especially when you're using two different data sources then you go for blending because even if you are not having even if you have same level of detail as well when you're using two different data sources you even you don't get those option of uh joins and the relationships as well. If you see here in the example, you are choosing two different data sources. One data source is this. If you go to the data source, we are on the regional targets data source. This is separate. Here you don't see the other data source that you have the product sales. Right? So here all these joins relationships that is related to one data source only. If you go to the this and if you go to the uh data source here regional target we saw product sales if we need to go to the product sales it'll take you to the product sales and here you don't have the regional target right so this is blending is you need to use blending whenever two different data sources are there there you can have the logical You can have the relationships. You can use the logical layer as well for two tables. You can use blending as well. One of the most important difference here is that you cannot use relationships joins when you are using two different data sources. So when you connect to a data source and if you're using two different data source, one is coming from an Excel. For example, let's say one file, one data is coming in an Excel file, another is coming from a SQL database. These are two different data sources that you're connecting to. You cannot do any kind of relationships or any kind of joints between these two data sources. There you need to use blending. There is no other option. Okay. So we learned about the combining the tables. We learned about the joins unions relationship. [snorts] We learned about that. And when you and we learned about data blending right. So whenever you are getting the data from one source you use the joins relationship blending right. So whenever you use when the tables come from a single source you can do joins you can do relation union you can do relationship. When tables appear from two different data sources then you can do data blending. Okay, [clears throat] data blending in terms of the transformation uh we learned a a few transformation like you can do a split column that we learned. There is one more transformation that you can do in Tableau which is you can do pivoting and unpivoting that is feasible actually you can do uh unpivoting you don't have pivoting option okay let me show that to you here so we'll open a new workbook okay we'll open a new workbook and we will use this example called piv pivot unpivot example right so this is the same example which we also learned in PowerBI okay so here revenue by year for example we have the data coming like this so you have year- wise revenue or zones and you have these revenue coming in multiple columns right so we learned in powerbi if you remember that these can be transformed we can give get these into single column column and we can have one column for the year, one column for revenue. Right? So this is called unpivoting. You can do unpivoting of columns. So that feature is available in Tableau directly. So how you can do it? Just select multiple columns here by pressing the control key and select. So I have selected these four columns here and I can do a pivot. Okay, pivot. If I click on pivot, it will now just do the pivoting of your fields. Right? So you got this pivot. You got this column here, right? Pivot. Now we need only the year part from it. So that we can handle. We can go to split. Okay. And we got this year part from the split. Rename this to year. Rename this to revenue. And you can give this field or you can hide this field if you require. Okay, you can go ahead and hide it. So you'll get you can do this way. You can get the data unpivoted. you connect it to the pivot and unpivot example file and we selected this revenue by year table. Okay. So this has these yearsly revenue coming in multiple columns. We need the year in one column and the corresponding revenue in another column. That's how our data should be. So here you can unpivot the columns that is feasible. Which means if you have data in multiple columns you can unpivot them and bring it in one column. So you can select the columns which you want to unpivot these four columns you have selected and just go and do a it will the name here is pivot you're pivoting okay so it will just pivot your data basically in other terms pivot means that you're changing the direction from rows to columns >> [clears throat] >> Select multiple columns pa then only you will see the pivot option. So here in Tableau you can do this pivoting uh but you cannot do the opposite of it. So unpivoting is not available that is the limitation. Okay, this is available in PowerBI. In Power Query, we can do both. But in Tableau, you can only convert multiple columns into rows. But the opposite is not feasible. Okay. Now let's deep dive more into the fields, understanding the fields and learning more on the visualization part. Okay. So once again open a new file and this time once again we will use the sample supertore data. Okay. So we'll create a new one and we will use sample supertore data. Okay. Open new file with sample supertore data. Use orders data here. Okay. Orders data. And you can go to the sheet. open this Tableau workbook with sample supertore data with the orders table. Okay. So in Tableau there are the fields that we are bringing directly from the data and there are some automated fields which gets added once you load the data in Tableau. Okay. So there are minimum three fields that gets created automatically and maximum five fields that get created. What are those fields? So if you notice here you have the data and you have the dimensions and you have the measures. So there are these five fields that you will see which are not in the data but those got added here. One is measure names. Now you did not have it in the data but you can use it in the visualization. You did not have latitude this is automatically generated. Longitude this is automatically generated. You have orders count which is count of rows in the orders table. Orders is the name of the table and count is basically row count. Okay. Okay. So it is giving row count as a separate measure and then measure values. Okay. So these five fields gets automatically created. So I said minimum three fields and maximum five fields. And here we can see all the five fields. So whenever we have any geographical field then these latitude and longitude these two fields gets created. So automatically if you have any geographical data like this or postal code with the type geog geographical role then it will generate the latitude and longitude automatically. Okay latitude and longitude gets generated and whenever you are visualizing any data on the maps it uses latitude and longitudes to create those. Okay. In if you remember, we created the map visual. Let's go back to that map visual. Okay. And if you see in the fields, it actually used these latitude and longitude fields. We did not select that, but it used those fields to pinpoint these dots in the map. Okay. So these are automatically generated fields latitude and longitude which we can use. Okay. If you do not have geographical data then these would not be there but at least these three would be there. Measure names, measure values and row count. Okay. Measure values and measure names. These fields are used whenever you are specifically you are using multiple measures in the visualization. Then the measure names and measure uh values fields come into play. We will cover that in a bit. But firstly remember that these are all autogenerated fields. So if you ever get this question which all fields are autogenerated in Tableau then you should know that there is measure names measure values row count latitude and longitude. These are the five that gets automatically generated. Now let's say I want to create a visual now and let's understand how we can create a visual from the scratch. So I'm going to start with a text table. Okay. How we can create a text table in Tableau. So text table let's say I want to see how um what is the total um sales profit quantity sold information is by each subcategory. I want to know this information. So what we will do is I want to see this by subcategory. So I'll drag subcategory into the row shelf. Okay. Subcategory got added in the row shelf. Okay. Now I don't want any visual like a bar chart and line chart which I get if I select any measure into the column it will convert into a uh a bar chart. I don't want that to happen. I want to visualize this in the form of table. So in that case you can use this text mark. Okay. Text mark and you can bring any field from here onto the text mark. Okay. and see if you're able to see sales in the form of table. Sales by subcategory in the form of table and not by the graph. So we got this created over here. Now I want to create I want to have another one and I want to create a table over here with the name of these measures appearing right now. It is saying just the subcategory and there is no label for it. It is not giving that label like what is this? Okay. So I want this to appear. So for this to appear we need to use these virtually created uh this automatically created fields called measure values and measure names. Okay, measure values and measure name. So what I need to what I will do here is drag any other field profit and drag and put it on top of this field. Okay, drag. Let me show this once again. Any other measure just drag and drop it on the this value section and you will notice that a lot changes in how the configuration of this visual is. Are you able to see the same output as I'm seeing? Okay. Now you see a lot of things have changed over here. You can see that in the detail in the detail there is in this text there is measure value has appeared over here and the list of measure values that we have selected sales and profit. I can see those over here. The label of the measure names have appeared at the top. So the measure name has appeared over here in the column measure name. Okay. So name all of these measures these measures contains the value and measure name field is always used whenever we want to display these measure names onto the visual. Okay. Measure names would appear over here. And this is specifically used when you want to use multiple measures onto a table. visualization. Also, this measure name this gets this measure name actually contains name of all the measures that you have in these measures list and it now chooses what we want to display here. How? Because we can see this measures added in the filter pane. Measure names have added in the filter pane. Okay. So what happens here is it has automatically [clears throat] identified that we are trying to add multiple measures and it has shown that okay out of all the measures I need to display profit and sales. So there is a filter that gots applied that measure names only show two measure names here profit and sales. If you want to see how this is working. So in the profit in this filter shelves you can go to the drop-down and you can go to show filter. Show filter. Okay this filter. Show filter. Just by selecting show filter it will show this filter as what we are showing and what we are not showing out of all the measure values. Now directly without adding anything from the fields from this filter of the measure I can choose if I want to show quantity I can just select and another field for quantity will be added. If I need to select discount another one for discount gets added. Okay. So from here I can choose which field I want to show, which field I don't want to show directly in the measure filter. But the step for creating this would be like this. This is the easiest way to create this. Whenever you want to display the measure names in the t in the tables whenever you have created anything and you see that it is not uh like you have made some mistake, you want to restart. Okay, you want to restart, you can always go and clear the this. So there is this option of clearing visual in the quick access toolbar. Clear all the visual whatever that you have created. Clear the sheet from here. What we wanted to do? I wanted to do subcategory wise. I wanted to see my measures. So I added sales first of all in the text field. Then I added profit on top of this. Automatically the measures appeared. I wanted to add more. So one way is that I can add now here in the measure values I can add more. So I can have discount over here added over here. This will show or quantity over here. Measure values. I can choose this or I can use this measure filter. Show filter. And I can choose which one I want to show. And I this is also changing these measure values over here. What I'm choosing to show and what I am not choosing to show. So this is how you can create a text table with multiple measures. Ron will come to that. We'll come to that one by one. So this is text table. Okay. Now let me create another sheet here. And this time I'm going to create a table with the similar table but with the some color coding into that. Okay. So once again the same thing. I added the subcategory here. I added the sales profit. I added the other measures here. Quantity discount. I have this. Okay. Now I wanted to do some color coding on these. Okay. I want to do color coding. So I can simply bring the measure values and drop it on the colors shelf. Drop it on the color shelf. Now I have added this color coding. Now wherever we are putting the fields in on this visualization on whichever mark type you will see that okay this measure value is being selected for two types of marks right for the text data for the colors and now I want to in this um for this measure value for the colors I'm seeing this colors applied on the text okay color applied on the text. So if I want to change the marking of this, I can go and make the changes in the marking. I can select this from automatic to a square. Okay, square. So if I choose the marking to from automatic to square, it will color the square. Okay, it will color the square. square is basically the background of this this cell where this text is. So now I can see the color coding by the all of these measures. Now can you tell me is this color coding actually correct? Is this how I should be color coding my data? Zelda is saying sorting may be required. Yes, that's good. We should always be trying to sort the result. Problem is one scale applied to all measures. Problem is one scale for all. You have one single scale for the range of all the values and you have applied to everything all the measures. Each measure for each measure your range of values are different. You cannot compete the values in the quantity column with the sales column. They will always be quantity will always be lesser because sales is quantity multiplied by price. So sales will always be much bigger. The number would be much bigger as compared to quantity. So quantity is all coming in this grayish color because all the values are almost negligible as compared to the sales. Right? So that's why everything in really big like dark blue is either coming from sales and some coming from profit. But if you talk about discount or quantity, these are very small value as compared to sales. And that's why this range is not telling us much. 567 quantity. Uh, sorry, not 567 quantity, but maybe 59 5,974 is a very good quantity figure for binders. But it is all showing that it's colorcoded with low value, low scale. So this range one scale applied for all the measures is the fundamental problem here. Okay. So what you can do here is you can change this measure value here where the color is applied. Go to the small drop-down here and choose this option of use separate legends. Use separate legends. When you go for use separate legends, there will be individual scales for individual measure. New scale for discount. New scale for profit. New scale for quantity. New scale for sum of sales. Now you can choose which color you want it to be in. In blue color, red color, orange color, purple color, whichever color you go. But now there is a scale individual scale being applied. Now I know when it comes to sales then these are the highest one right amongst the highest one above 300. when it comes to profit then I know that copier are having the highest profit right followed up with phones then accessories so I can identify these when I'm applying individual scales similarly if we go to the quantity wise quantity wise binders are doing much better papers are doing at all. Okay. So I can see these different scales now. It's much better and it is telling me the true picture of this high low values. Okay. S. So what we did? How did we do? There is an option in the color code where we have the measure values and the color code. You go to the small drop-down and choose this option of uh show separate legends. Right now this is saying combine legends because we have selected show separate legends. So here there is an option of show separate legends. Click on that. Now for these color gradients that we have if I want to give some other our own color gradients. Okay let's say I want to give my own color range then I can do that too. So for individual all of these let's say for the sales we have this range. Okay. If I want to give it some other color I can go to this scale and I can go to edit colors. Okay. In the edit color I can choose what type of color range I want. So I can choose first of all the color. Let's say I want this to be shown in um green color. Let's say you want to use a green color. So it will create a gradient in the form of green color. You can apply this. Now sales would appear in this green colors gradient. Right? Similarly for quantity if you want to apply some other color you can go edit color. Let's say for quantity you want to go with some other color. Okay you can go with any other color. Let's say I go with blue color. Okay. So it'll appear like this. So you can give your own color ranges for each of these legends. When it comes to profit, we would know that in profit there can be scenarios where your values can be less than zero and greater than zero. Now you would want to identify those specifically that whenever it is less than zero it should be in some other color. If you just put a gradient then it may not be able to do that. So what you can do is you can go to edit color here and you can specify whether you want to have let's say you want to have a range and whether anything negative and positive can be specifically identified. So you can have a color type. Let's say this is the range and you want to give a a midpoint here. So you can go to advance and you can give a center. Let's say center is zero. Okay. And there should be center needs to be identified. And it should just give the values uh below zero in some other color gradient above zero and some other color gradient. Okay. Similarly for discount you can also do the same thing. Okay. So this is called your highlight table or table with conditional formatting. Both means the same thing. Okay. Another way with which you can use the highlighting is when you have too many data points and you want to use you cannot see the values as such but you want to see a heat map. Okay, you want to see a heat map. So there you can use the colors as well to just highlight what kind of whether this you have some good values, bad values, those kind of identification you can do. So we'll go to the new worksheet and this time I'm going to use um let's say I want to use how the performance of subcategory sales are [clears throat] across all the states. Okay. So what I need to do I want to use say states in the rows. So you'll have all the states over here. You'll put subcategory in the column. So you'll have all the subcategories in the column. And I want to use let's say I want to see profit. How profitable I have the subcategories. So I'll use profit here and I'll put it in the color mark. Profit in the color mark. I'm able to see all of these just the color. I have not added any value anything in the text label. I have not added just the color. So no values being displayed over here. Just the color indicator is coming. Now I want to see the full range of values here as well. So I can go my view change it to from standard to entire view. it will show the entire view. Now is this something that is whenever you want to see a more detailed like just very high level information about the how your um business is doing across the different region. I can get some highlevel idea directly with these kind of heat maps. Okay. For example, if my question to you is look at this heat map. First of all, create this. Now, let me just with this answer my question, which is the state where you are seeing consistent profits across the subcategories? States where you have consistent profit across subcategories. Yep. So we can identify just by even if if we don't look at the numbers we can identify these kind of details with the heat map. I can with the heat map if there is something popping out I can see that very easily. For example, if I need to know which subcategory is not performing good in most of the areas, which are those according to you, which subcategories are not performing good, right? So, these color codes are telling us which is not performing, where the profits are negative or positive. In copers, I don't see any negative profit anywhere. Okay. White color indicates that there is no sales, there is no data point. Okay. So we can identify these informations. We can identify which is the worst state, which is the worst categories, which subcategories, which is consistently low across, which are good in most of the areas. But in some areas, for example, if we talk about we talked about two states, California and um California and New York in both of these all of the categories subcategories are positive except for the tables. Okay, let's look at this in the next example. So here, so I'm going to use the map visual here. Last time we we use the bubbles on the map. This time we are going to use the field map. How do we get to that? So first is any kind of detail that you want to see on your visual be any kind of uh categories, you can bring it into the details shell. This will add some values on your uh worksheet. So remember to visualize anything on the map you can go to this you can select country on the details and automatically automatically this will appear on the map. Now what it did is because it identified that country is a country is a geographical data. So it used this latitude and longitudes. Okay, these are dynamically generated latitude longitudes and it pointed it on the map. Okay, it pointed it on the map. Now this gave me one default map visual that is there that we had here. If you need to change it to some other map visual or any other visual, another way that we can go about it is this show me panel. Okay, show me panel we haven't covered so far, but it is very powerful, very useful, not powerful, but very useful when it comes to creating your visuals. So, if you expand show me panel, it [snorts] tells you the 24 visualizations that are there. It gives you all of those. It gives you detail about all of these visual with how many dimensions and how many measures are required to build any visual. If you hover over for example this text table if you see at the bottom it tells you one or more dimension one or more measures is are required for you to use this. Okay. Then heat maps you need to use one table. Okay. And one dimen one dimension one measure minimum. Then highlight tables you need to use one dimension one measure. For this symbol maps you need to use one geographical dimension that is required and zero to more measures. So right now we have not selected any measure. We have just selected one dimension that's why it is appearing. So for all of these different types of visuals it tells you which dimension how many dimensions how many measures you require for you to use it. Now when we selected country in the details it automatically created this latitude longitude and it gave me this map but I want to use this field map other map. So the only way that you can go to the field map is through this show me and now you switch and switch your visual to this one field map. Okay, field map. What this do is now it fills all the region with some color. Okay, now I want to add more details over here under country. Let's say you add state as well in the detail. So go add state to the detail. Okay, now you have the states appearing here. All of these boundaries are now coming by state. You are able to see state. But at the moment these states, all of these are just without any further details. Just the label of these states with a default blue color. I want to highlight this by the profitability which is more profitable. So go to profit give color to this. And now it will tell you which state is more profitable, which state is less profitable. So similar way how we have created earlier we gave the color coding but that time we use the this symbol map where we can use some shapes and the default shape was circle right or the bubble. And here we are talking about the field map. Now in the field map we can also add some more details. like I want the labels to appear over here. I want to have some labels. So this mark is for you to show label on the visuals. Okay, label. So let's say if I want state to be labeled as well. So you can go put it on the label and you will have the name of the states appearing on the chart. Let's say you want to also see the sales for these from these maps. [snorts] You want that to be appearing here as well. So you can bring sales add this to the label too. Now you will have the sales as well as the name of the state and the color coding is based on the profit. Now you see that with this way this some of these labels are not appearing because of the size is too big. Your text size is big. I want this text of the states to be smaller. Okay. It should occupy less space. It should be smaller. So you can go to this label mark. Okay. Go to the label mark. And here if you click on it you get the formatting option of the label. Okay, one of this is in decrease the font size. So we can go and decrease the font size to let's say I decrease it to eight. Right, we decrease the Okay, if you want to decrease further, we can type and decrease it further as well. If I want to decrease it to six, I can type and enter and this will be even lesser. The text that is appearing over here, this text is appearing like we have the state name come then the sales is coming. If I want this appearance of the text to be slightly different then I can go here in three dots and I can choose how this should appear. If [clears throat] you want this uh state name after the sales you can do that. So this is how it is coming. So if you want to add some more formatting over here, you can do so. Let's say before the parameter which is sum of sales, you want to have that just an label that we are showing sales you can type sales put a colon something like that and apply. So it will also show that sales is equal to this much so that you don't get confused but you should always avoid having too much of text on the visual. Now the sales is being repeated in all of these states I don't like this. So for the users who know that we are displaying we're using sales they will be get they'll get a custom. You can add this detail but repeating the same information is always not recommended but you can do so. If you want the font size of the state smaller name of the state but the font size of this measure sum of sales bigger then select only this part and increase this font size of this to eight. And now the sales value is bigger but the name of the state is smaller. Okay. So these kind of label formatting you can do on the visual. Right to your question only few states and their sales are appearing on your map. That is because of the if you have two big text size then it will not show all the value. If your graph is small and your text size is bigger, it will not show. Okay. Do one thing. Look at this in the full screen. Click on it and look at this in the full screen and see if you are able to see more states and the values. So this for the color coding we selected profit and selected and dragged it and dropped it on the color mark. Profit on the color mark. Okay. You need to change the map type. You need to change the map type. So go to show me here. Instead of this maps, use this map. Okay, use the field map option. So we learned about how we can create the stack bar charts. Let's just once again look into that. Okay. Now here I would want to see how my regions are performing. Okay. And within the region, what is the contribution of each category within that region? Okay. Okay, I want to see that how is the contribution of each category within the region. So we have this region. I can bring that into the rows. I can bring sales into the column. Right? I can make the view as entire view. I can add the label. Correct? Now I would want to see the breakdown of each region by the category. Okay? So I can bring category into the color mark. Okay. Now I can see what is the contribution of each category within that region. So just um we use the this option data label not the tool tip. So use the data label option. Select this option for the data lo label to appear. Okay, now I'm able to see the values of these all the categories by the region. But let's say I want to see these values in the form of overall percentage of total values. How much do these values are? So in Tableau there is a feature called table calculation. Table calculations those are just easy calculations that you can apply on top of the results that you see on the on the sheet. Okay. So for example this is the result that I'm seeing on the sheet. Now instead of showing this in the form of numbers I want to see this as a percentage of total values. Each of these is percentage of total. So this measure that we have here, you can go to this small drop-down for the measure on top of the measure. You can go here and you can move to table calculation and you can go to a quick table calculation and you can change it to show the percentage of total. percentage of total. Okay, if you convert this to a percentage of total, you will be able to see all of these in terms of percentage like this. Now, out of all of these, we can know that of course West is having the highest sales but actually the biggest sale for the category any category is in the east region. which is technology. Technology in the east is having the highest amongst all of these 11.5%. If you want to so show both the values as well as the percentage of the overall total you can do that too. Okay. So what you will do sales you drag it on top of the label. So you want to see the sales in that label. Now what happened in the label you are only seeing the sales value not that table calculation table calculation is for the column value. Now this is only displaying this access here but the label is being shown in the form of the sales. I want even the label to show the percentage of total. So you can add sales once again in the sorry you don't need to add it once again. You need to first change it to table calculation quickly here in the display. Change it to table calculation. Now this is modified to show as a table calculation. And now on the label as well you see this value. And to display sales as well as the figure once again add the measure over here. Okay. So now you can see the percentage as well as the actual values both coming on top of the on the visual. Okay, if you want this to just appear below that, you can change the sequence. Now the region wise sales show and tab view add data label. Okay, now if I want it to be sorted, let me also sort it. Show the sorted result. So this is sorted by region wise total sales. Now let's say for now I want just this sales to appear on the label to sorry I want this to be divided by category. So add category into the color. Okay category added to the color. This data label is already coming. Now I will bring sales into the data label as well. It will not change anything because sales we already selected the data label. But basically I want this sales not to be shown as the sum of sales but as a percentage of total. Okay. So go here and change it to a table calculation. Quick table calculation. Percentage of total on any of the fields. If a table calculation is added, you will see this triangle symbol. Whenever you see this triangle symbol next to any field, it means that some table calculation is being applied. This is still so shows sum of sales. But we have applied some table calculation here. Okay, this triangle will confirm that. So now another sales label you can add which is sum of sales just as it is. Okay. So just do that and if you want to change the order you can do that. So the next visual that we are going to cover is tree map. Okay tree map. So what are tree maps? You have seen that in powerba as well. What are tree maps? So tree maps allows you to show contribution of like when you have a any value it could be sales it could be uh orders any kind of measure if you want to see what is the contribution of each categorical data into that measure value and you want to see everything in a single view. You don't need to scroll your visual just whatever defined space that you have within that. If you want to see all the data points then you use tree map and it basically lets you see which are your top contributors and which are your bottom contributors. How will we go about remap here? We need to first select any category. Okay. Anything that with which we will want to see the distribution of our data. Now let's say I want to see first the category. Okay. First category. So I'll bring category into I'll select category. Okay. Another way that you can do here is because tree map you will not get it directly from uh by adding to rows and columns that will come from show me panel. Okay. So you'll need to select show me panel here to be able to use stream. So what you do is you select two fields. So you have selected category and then you select sales. Okay. And press control key and select. So now you see here I've selected category. and I have selected sales. Now I have selected one dimension and one measure. And in the show me panel I'll be able to see all of these visuals which supports one dimension and one category sorry one measure one dimension and one measure. Which are those? Which are those? So table highlight table this table pie chart these. So some of these visuals support this which are highlighted over here. We will select remap from this list. Okay. So go ahead and select remap here. Right. It will create map directly. You should be able to see your data like this. Now if we need to uh see the data in more detail, we can add further hierarchy in our tree map. So currently it shows technology being the highest when it comes to sales about 8 uh 836,000 followed by furniture and then followed by office supplies. So in tree map your always results will be in this sorting from left top to right bottom. Okay. Highest value will be over here. Lowest value will be over here. Okay. That is how premap shows the result. Now if I want to see along with the category, if I want to see subcategory as well, we will add subcategory onto the label mark. Okay, add subcategory to the label mark and see if you are able to see detail at the subcategory level. Okay, subcategory onto the label. Okay, are you able to see the result like this? So you have data for the technology categories and all the underlying subcategory within the technology over here. Okay. Within this block, highest would be phones and then lowest will be copiers. Similarly for furniture, chairs would be the highest, selling one and then uh furnishing would be the lowest and so on for the others. Okay. So what did we do really quickly once again? I'll [clears throat] show this once again. So I'll remove all the visual. We selected category. Then we selected sales. We selected two of these measures. We can see all the possible visuals that we can create here. One of them is tree map. So select tree map. This will show category wise sales information. Automatically it has created this sales with the color gradient color and size with the size based on the sales as well. Right? So this configuration automatically happens once you select tree map and then we added subcategory into the label text. Now I am able to see these labels here. I'm able to see the labels. So technology, phone, all of these labels are visible. But I would also want to see the actual sales values, right? So I can bring sales as well onto the label. Okay. Onto the label. And we can see the labels as well. Okay. So here in this visual we add first we have added category and then we added subcategory. Okay. So in when you uh talk about dimensions in your data some dimensions always are hierarchical in nature. Okay. Some dimensions are hierarchal in nature. [snorts] So can you give me example of any hierarchical fields like the fields which are which have hierarchy which falls under some hierarchy in this data. Can you look at it and tell me if there are any fields which are part of a hierarchy? Okay. So in this data I think this is good enough. So when it comes to hierarchical hierarchy you uh so you give correct examples. So here we have these fields which are part of hierarchy. Hierarchy they are there is a natural hierarchy in the relationship between these. Now first let's talk about the product hierarchies. Okay product hierarchies we gave example of category subcategory. We also have product here. Okay. So wouldn't product be part of the hierarchy too? The product will be the hierarchy as well. So here the hierarchy could be you will have category under category you will have subcategory under subcategory you will have products that is the hierarchy. Then you gave the example of geographical hierarchy. Okay. In geographical hierarchy what is my hierarchy? Here I have data by country. Then after country the next hierarchy is state. Okay. Then after state you have city. Okay. And then uh in this we don't have anything more granular than city but that is our hierarchy. So it will be country, state and city. That will also be a hierarchy. If you notice when we select any date related field Tableau automatically helps us use that hierarchy. Okay. So if you remember when we select any date over here let's say the order date we select here we already have this hierarchy built in. So if you see this is showing in the year automatically the year part comes in. And if you see once this hierarchy is there you also get this plus button over here. Okay this plus button. So this is this basically allows you to leverage this hierarchy. Just by clicking on this plus button it takes you to the next level. Then it takes you to the next level. Then it takes you further to the next level. Okay. So this is how this is how the fields which will have hierarchies defined would work. Okay. Now we know that category subcategory product are part of hierarchy. So when you have such data then you can define the hierarchies in your data so that when you use them in any visualization you can use the uh this hierarchy feature. Okay. So how do we do that hierarchy plot? How we define hierarchy? So here here you have let's say I go to this small drop-down over here uh sorry not here but let's say category just go to this and do a right click on the category right click on the category okay and you see this picture of hierarchy and just click on create hierarchy okay create hierarchy now let's you can define what this hierarchy name would be let's say this is called you can keep it as product hie