Data Analyst Roadmap 2026 | How I'd learn Data Analytics in 2026

Key Takeaways

Data analyst role is considered as a gateway to enter data and AI field. However, the role is constantly emerging, trends are changing, and you may have this confusion. Is it a good role to pursue in your 2026? And if it is, what are the skills that we need? Now, if you go to YouTube, you will find so many videos. Some of these videos are created by people who themselves don't have any real industry experience, and they are unnecessarily hyping things up. When we started making this video, we wanted it to be based on absolute reality. So, we did interesting experiment. We analyzed 1,000 plus latest data analyst jobs from prominent job portals and created a list of the skills which are in top demand. Then, we combined that with our own industry experience. Both myself and Hamon and Vadivelu, who is the co-creator of this video, have 10 plus years of data industry experience. Hamon and especially was working as a data analyst in Europe for few years. Then, he become a data analytics lead, where he was managing a team of data analyst, and he worked on more than 30 enterprise projects. And then, we also consulted several data analysts who are working in the industry. We talked to data analysts, their managers, VPs, and directors and took their opinion. On top of that, we combined our own experience of working on AI and data projects in my data and AI consulting company, Atli Technologies. As a result, we have prepared this practical roadmap with week-by-week study plan using free learning resources, checklist, and assignments. Now, I'm not going to say anything unrealistic to increase the views of this video. This will require 4 hours of study for next 4 to 6 months. And as you can see, there is a lot of hard work that you want to put in. And if you're looking for any shortcut, please leave this video right now. On the screen here, I'm showing you some salary range for data analyst role. So, I searched for data analyst, and here you can see the salary range. We also created this histogram so that you get an understanding. As you can see that most of the salaries are in range 3 to 15 lakh with some salaries going in the range of 1 crore as well. There are, I think, 14 jobs which had more than 1 crore salary, and these are the directors and, you know, head of data, etc. In the US, this is the range. Folks, you can go to these websites levels.fyi, Glassdoor, naukri.com, etc. at any given point to get an idea on the salary range. And if you want to know how many jobs are available, it is simple. You go to any job portal. Let's say I go to Naukri and search for this data analyst job. I found total close to 30,000 jobs. Now, see, they post these jobs using different titles. So, data analyst is one title, but there are different titles as well, such as Power BI developer, for example. For that, we found some 46,000 jobs. There can be operational analyst, finance analyst, and so on. So, this roadmap is not valid just for general data analyst. It is also valid for function and domain specific and tool specific roles as well. And we are going to look into the categories, different types of data analyst little later. Before proceeding further, if you have doubt if data analyst is a good career to pursue in year 2026 or not, then please watch this video. We are going to provide you a link, and after watching that video, you can resume this roadmap video. In that video, we analyzed reports, we spoke with experts, and we provided a detailed recommendation on how to proceed. And one important thing you need to figure out is if this career is suiting your natural skills and interest or not. For that, we have created a free survey where you will answer a bunch of questions, and it will tell you if this career role is good for you or not. Now, folks, here is a tool we created to analyze the hot skills in the job market. It's a free code on GitHub. You can download this code and run it anytime. And this Python notebook shows you the hot skills for data analyst role. So, here you can see the skills such as SQL, communication. See, communication is a very common skill, Python, and so on. And then, there are different role titles, right? Like marketing analyst, for example. For them, these are the skills. Finance analyst, for them, these are the skills. So, we are going to provide you a link of this code repository, so you can take a look. And in the future, if you want to run this report, you can do it on your own. So, here are the tech skills and core skills. As a data analyst, you need to build both of these skills, and we are going to provide you a week-by-week study plan using free learning resources, where you can study both the category of the skills. Uh in tech skills, you find some of the obvious things such as Excel, Power BI, Tableau, SQL, etc. But we want to highlight this point about data engineering basics. Since data analyst and data engineering roles are kind of merging, it would be good to have the basic understanding of data engineering fundamentals, some of the cloud platforms such as Azure, AWS, Databricks, etc. if you want to become a full-stack data analyst. And then, there are good to have skills as well, right? So, if you know of Microsoft Fabric, Databricks, Alteryx, etc. on top of the required skills, then that can give you unfair advantage. All right, now let me hand it over to Hamon and to talk about the data analyst job categories. So, we looked into the hundreds of job descriptions available there in the job portal right now and divided the data analyst role into four major categories. So, these categories are BI reporting analyst, tool specific analyst, domain function specific analyst, and a full-stack analyst. Now, I will explain each of these categories with an example. Let's go into the first one. So, here you would see the job roles would directly say that you are into reporting. It will clearly mention. It will either say reporting or it will say something like MIS executive. So, when you see this word MIS, just understand it's about reporting. MIS means management information systems, where you would be collecting a bunch of information and convert them to reports. So, when I say reports, these are most likely to be standard reports. You're not going to create a new form of report. There will be some existing format of the report, and you will be creating them. And you can also typically see in these job roles, they would mention multiple BI tools, like Looker Studio, Power BI. All these are BI tools. Why do they mention multiple tools? Now, you need to understand this. Because most likely, these roles are a central IT team, right? A central IT team means there is a typical team which is sitting as a function and supporting all the other teams. So, the IT team would support the marketing team, sales team, finance team, or supply chain team, those kind of things. Or it could be a service company which is supporting multiple clients. That's why they would want you to understand uh multiple tools, use multiple tools. So, here the thing is, you won't create something significantly new, right? So, that is why this particular role, this BI reporting analyst role is more fresher friendly. So, if you are a fresher with minimum experience, if you have a portfolio or something like that, most likely you will get a job as a BI reporting analyst or an MIS executive. Now, let's move on to the second category, which is the tool specific analyst. As you could have already understood, tool specific analyst means it points to a particular tool. It could be like a Power BI developer, Tableau developer, or a Looker Studio developer. So, it will clearly say that you are going to work on this tool. All your work will be based on this tool. Here again, there will be standard reports, but you'll be working only on one particular set of tool, and the the depth of work will be much higher here. Within Power BI, you would be going into the depth of its use cases. In the previous role, in the BI reporting analyst role, you would be using multiple tools, but you will be creating most likely some basic to mid-level reports, not advanced reports. But in this role, you might be creating advanced reports in Power BI, Tableau, but again, the use cases will be predefined. You won't be creating new reports here. And this role is also fresher friendly. If we talk about freshers, most likely as a fresher, you would get a job in these two categories. As a junior Power BI developer, a junior Tableau developer. So, when you're looking for jobs in Naukri or any job portals, look for these two particular categories if you are a fresher. I'm not saying you know, other people who are experienced won't get a job here, but this area is more friendly for freshers. Right? And so, you can see an example here, right? They would ask you to create Power BI dashboards, semantic data models, SQL views, stored procedures, ETL processes. This is something a fresher can do pretty much easily. And whenever you see the experience mentioned as 1 to 5 years, when they say experience 1 year, as a fresher, you can apply, but always apply with a portfolio. Show the proof of your work, right? Which is something we will discuss in the later part of this roadmap. And then comes the third category, which is domain function specific analyst, right? It could be a retail analyst or marketing analyst. So, if you don't understand clearly what is the difference between a domain and function, please check this post. I've made a post on this, and I'm going to give a link to this post as well, right? So, you need to know what are the domains, right? Domains are nothing but like domains like consumer goods, healthcare, automotive, gaming, energy, banking, finance, securities and investments. These are industries, right? What are the functions? Functions are like sales function, finance, marketing, supply chain, and uh human resources, customer experience. These are functions. So, these functions will exist within all the domains. So, you need to understand the domains and functions very clearly because each of these functions and each of these domains would require an analyst to help them in developing insights so that they can make a business decision, okay? So, the first step for you is to understand the domains and functions very clearly. I'm going to give a link to this post so you can go and watch it later. So, you can see an example here. It clearly says they want a retail analyst, which means they expect you to have some domain knowledge in the retail industry and functional knowledge in the marketing. So, they're looking for a marketing analyst in the retail domain. So, if if you understand the basic KPIs of marketing like customer acquisition cost and customer lifetime value and you also understand the retail retail business, right? Like any retail business or FMCG business, if you understand how this business is run, you're going to have a massive advantage here. So, don't think that uh you are a fresher. If you're a fresher, you won't be able to have that kind of a knowledge. There are ways to build domain knowledge even if you're a fresher, which will be discussed in the road map later. So, now coming to the final category, which is the full stack analyst. This is the category which is emerging these days. It has been there, you know, since last 5 years, but off late, in the last 1 year, I would say, it's emerging. It's becoming more evident that data analytics and data engineering, they are both combining together. So, as a data analyst, if you have understanding about cloud technologies, like if you understand what is AWS, Azure, [clears throat] if you understand how things work before it comes as a data in a table, right? So, mostly as a data analyst, you will connect the Power BI dashboard or Tableau to a database table, but let's say that you understand how to bring the data to the table, then you are going to have a massive advantage and these are the people companies are looking right now. They're looking for people who have familiarity with the cloud platforms. Like you can see here Azure, GCP, Snowflake. So, with these kind of things, what you will become is an end-to-end analytics engineer. Of course, you won't become a fully fledged data engineer at the beginning. You might understand few things. You will still need a support of a data engineer, but if you're someone who can understand these things at least at a basic level, you will be able to work better with data engineers. And these kind of people are in demand. So, if you're a fresher or if you're an experienced person looking to make a career transition, along with data analytics, having basic idea of cloud technologies and working with things like Databricks Fabric will put you in advantage. So, just to summarize, these two categories are more fresher friendly and uh these two categories are less fresher friendly. That doesn't mean like uh you know, you can't apply as a fresher to this category. All these categories have depth, you know, you can become a senior reporting analyst, you can become a senior Power BI developer, or you can become the head of the analytics department where the department is working only with Power BI or only with Tableau. I've seen all these kind of roles here. And domain function specific analyst, this is something a product company would prefer very much and if you are someone who has a lot of experience in sales, marketing, supply chain, try to become a domain specific or function specific analyst. That will give you a lot of advantage because with AI taking over a lot of you know, manual task, having that domain knowledge, having that functional knowledge gives human beings a lot of advantage. So, if you think that you have the domain knowledge or functional knowledge and try to become an analyst, try to learn these tools, which is Power BI, Excel, SQL, Python, try to learn these tools and become a domain or function specific analyst, that's going to help you a lot. And for full stack analyst, I would also recommend freshers to try this. Freshers to become a full stack analyst because now that you understand all these tools from the data analytics side, if you try to understand the data engineering side of things as well, it will increase the chances of you landing a job. Right? I'm not saying this will guarantee you a job, but this is definitely going to increase your chances of you landing the job. So, what we did is uh instead of we just saying things, we thought of talking to people who are actually working as data analysts, who are actually hiring data analysts, the head of departments, the VPs and directors. We spoke to them. We asked them what competencies they look for analysts in 2026 so that they can hire them in their team. And we spoke to many people. I'm going to play a few clips to you so that you can directly hear from them. It's not the technical skills that are going to matter more. It's your problem-solving ability and the communication skills. I like people that are curious, that are always asking why and what if. Um [snorts] I need people that are hungry to learn, that are constantly uh trying to not be comfortable, trying to get out of their comfort zone to to where you actually grow and develop yourself with an open mindset. So, I would rather hire >> [snorts] >> people that are probably maybe fresher on the market, but they have the right mindset uh rather than bring someone with a lot of experience, but uh that doesn't have the right um attitude. Many candidates are great at crunching numbers, but struggle to communicate the insights effectively. Data analysis, in my mind, is not just about numbers. It's about telling a story that can uh drive business decisions. Domain knowledge is almost as equally as important as uh technical knowledge in many capacities in the world of analytics. Without domain knowledge, there's so much, you know, I'd say waste or inefficiency uh because you often spend a lot of time trying to absorb knowledge from business people who don't really have the time to teach the analyst about their job or what they're trying to accomplish. So, I I do think that business uh and domain expertise is critically important in many cases um as it enables you to be faster, better, and be able to speak and tell stories with the data and that what you're actually looking at. And if the domain knowledge is lagging, the next best thing that I kind of tell people is that natural curiosity is the next best best trait for an individual in analytics. It's something I look for and something that usually can't be taught, but if you don't have the business context or the domain expertise, I usually go for someone who is uh naturally curious because the curiosity aspect is what that allows them to dive deeper into that domain uh and be able to speak more intelligently about it. So, after listening to all these people, one thing that is absolutely clear is technical skills alone won't fetch you a job. If I have to be definitive, I can say that there is no future for those kind of data analysts who simply used to grab a bunch of information and create reports. That part is gone because those things AI can do now. Pretty much AI can do now. So, as a data analyst, the business people are expecting you to add more business value in terms of strategic collaboration, in terms of critical thinking, you know, you you provide critical insights to the business so that they gain some competitive advantage or as a data analyst, you talk to stakeholders and make their understanding easier. You know, a lot of people make this mistake uh you know, when they start as a data analyst, they talk very technical to the non-technical people. But as a data analyst, if you know how to switch gears, that you switch the technical gear with technical people and a non-technical gear with non-technical people, that is a massive advantage because there the collaborative spirit is very high and you can support a lot of people in the team. And for all these things, you need to stay very curious like I say, insatiably curious in order to continuously learn things every day and add value to the business. What these people said, I can see that it's also reflecting in the job postings these days, right? You can see the words like natural curiosity, domain knowledge in finance, which is which is very important and you can see these words like cross-functional collaboration and you can see about like engage stakeholders and meet expectations by value and actionable insights. All these all these things you can see, right? Collaborate with cross-functional teams. So, these things are very important if you're planning to become a data analyst in 2026, not just the technical skills. Again, technical skills are important, but that alone is not sufficient. I can explain this to you with a real experience of a project we did in CodeBasics around web analytics. So, earlier, what used to happen is like when we started 3 years back, our website did not collect a lot of data. So, what we used to do is collect the data source directly to Power BI and we used to build the visuals. So, we had data analysts in our team who did this, right? So, they were pretty much like connecting the source and building the visuals. But now, what they're doing? Now, the data has grown big, right? So, the same data analysts in my team, they kind of understood how to connect to the raw data source, how to connect the raw data source and how to connect with multiple other sources using APIs. And they created this ETL pipeline using Fabric notebook. So, this is where the data analysts in my team started becoming like data engineers, right? So, they started building this data engineering skill as well. And then they pushed it to data warehouse. Again, it wasn't easy for them. They have to learn few things in order to do that and they did some mistakes in the beginning. We allowed them to make mistakes and learn from it. So, they were able to work in an environment like a sandbox environment. They were allowed to make those mistakes and then push things to production. And then what happened is while making this push to Power BI desktop. By this time our team has grown big. We have finance team, marketing team, we have learner experience team. All this team is now using this dashboard. So, for the data analyst in my team, right? They have to now work with these people. So, this cross collaboration, project management, and you know, stakeholder management all the skills were exhibited by the data analyst in my team to build this dashboard, right? And they even applied business logic. They even went to the extent where they can save cost and enhance the user experience of people using this dashboard. So, what they did is like when the whenever the table is huge, they applied incremental refresh so that the business or the dashboard which the end users are using it's getting loaded very fast. Otherwise, when they click some button, you know, the load time is very slow. Now, with implementing all these things they were able to make a much faster working dashboard. And I can clearly say the future is moving towards the direction where all all the data analysts are learning more things. So, my the data analysts in my team, they are able to make automations using Power Automate, n8n. And now they are learning also data engineering. And don't think that they are spending more time at work. No, that's not the case because lot of things they were doing earlier like fetching the data manually, like fetching the data via email. They are now using APIs. They are now using automations. They are doing that faster. Whatever time they are saving with the help of AI, they are reinvesting back to learn new things and do more for the company. So, this is how they are expanding as a data analyst. So, if you are a fresher or an experienced data analyst watching this video, just know this that don't think that okay, why should I do more for the company? Why should I do more stuff? It's not about that. It's about like how you are like the best product for the market, right? It's a product market fit, right? So, treat yourself like a product. The product has so many features. Now, the market cannot resist you. They have to have you in the team. So, you have to build yourself like that. With that mindset, whatever job role you are trying in in the data and AI field, you'll definitely find success. So, in short I would say you can be jack of all trades and master of one. But if you cannot be master of one, still jack of multiple trades is better. But in general, my thing is like you be the jack of all trades and master of one. And you know, all these tools are lying out there, right? Like there's this SQL, Power BI, Python, Databricks. There can be many tools, you know. As a problem solver, you should not be worried about what tool you should use. For example, I learned Power BI when I have to solve a problem. I did not know Power BI is going to boom later. I I started learning Power BI in 2017. I was using Excel before. And I just picked Power BI as a part of my solution and it became big later. So, right now Databricks or Fabric could be such tool, right? Just learn tools as a part of solving a problem and gradually you will be in the problem-solving arena. Which will always make you relevant in the industry. Just to summarize, you need to aim to become a problem solver. This is the number one thing, right? Problem being a problem solver is the number one thing. And build strong technical foundations. It's important, right? It's It's It's a foundation. And you need to have this growing business acumen, which means on a daily basis you should learn how business works. Just think there are only three KPIs, revenue, cost, and profit are goals. Within these three KPIs, what are the sub KPIs you can provide to improve the business? To give a competitive edge to the business. That is all about data analytics. So, if you understand that, your business acumen will grow on a daily basis. And the most important of all, you should have that ability to communicate clearly to the stakeholders. When I say communicate clearly, do not misunderstand that with your English speaking skills or with your speaking skills. It doesn't matter if you are very fluent or it doesn't matter if you don't use a lot of vocabulary. That does not matter. What really matters is how clearly you are able to communicate, right? I've seen a lot of genius people in the tech industry struggling to communicate with top-level stakeholders, right? The top-level stakeholders, you know, when I sit in some of the board meetings, they get confused and they just think, oh, I don't understand the solution. I don't want to implement it. That's where they need a person who can communicate to them in terms of business value. They won't say like okay, implement Azure, implement Fabric, this is that. They won't talk about the the endpoint security. They won't talk about things like this. They will simply say like okay, fine. If you implement this within 6 months, our revenue can grow [clears throat] to this because of this, this, and that. This is how you switch gears and communicate to stakeholders. And this is a very important skill. So, if you look at this skill matrix, I've made this data analyst skill matrix for different kind of data roles. As you can see for some of the roles, you know, you you need a particular skill, which is SQL is kind of needed for all the roles. It's It's very high. But you know, for some of the roles some skills are not that important, right? Maybe for a domain-specific analyst they don't work much with Python, right? It's It's kind of low. This is purely based on the job descriptions available out there on the internet. Sometimes employers mix it up, right? They put all things together. So, ignore that. I'm just taking the general average and given this matrix to you. And like let's let's say basic statistics and probability something that you need to understand for all the tools to a medium level. And you can see that business metrics and domain knowledge is very high if you are a domain-specific analyst. For the roles, it's It's medium. And data storytelling is very important for almost all the roles. And data engineering basics this is a skill that, you know, as a full-stack analyst it's It's very high. You need to have the skills at a very top level. And in terms of stakeholder management, as a domain-specific analyst this is the number one skill you would have because you would constantly work with people who talk business. They don't They don't talk data. They talk business. You convert the Python code or SQL whatever you write to KPIs. So, that's the business language the stakeholders speak. You convert that to KPIs. You talk to them using that language. So, that's why this skill is very important there. And insatiable curiosity is like common for all. I would say this is a very high skill you would have for all the roles irrespective of whichever part of data analyst role you are taking. Same goes with communication. And being AI generalist, this is very important now. But when I say AI generalist, it means how you can use tools like Claude, how you can use tools like ChatGPT projects. And there are a lot of things within this tool, right? Like just don't think that you are giving a prompt and getting an answer. You know, you can go deep into these tools. And this is a skill that you should have as a data analyst in in general. And critical thinking like I always say it's very important for all kinds of data analyst. So, friends, this is the data analyst roadmap PDF for 2026. In this PDF you will get the week-by-week plan with all the free resources attached, right? For all the 16 weeks. And you will also get a proper checklist, a complete checklist for a data analyst role. And you would also get all the practical tips and guidance. So, let's begin with the first week. But before that, let's see what is week zero. I want you to be very clear about this. You are getting into this preparation and the foundation of your preparation should be based on research. It should not be simply based on what CodeBasics says or what I say, what your friend says. No. It should be based on your own research. Trust me, whatever effort you are going to make in the preparation if it has to get the actual outcome that it deserves, it must be based on your own research. That's why I want to build this research mindset with you. So, we are going to start the week zero with the research. These are some of the research questions checklist that you should fill, right? You need to understand if there is a growth in the market, are people getting job roles, blah blah blah. All those things you need to understand first. And of course you need to refer some standard reports, right? The World Economic Forum report which I'm going to show you now. So, this is one of the standard reports that you can go through. You can study especially the page 19. It gives you some idea about how data analyst roles and data scientist roles can grow. You can understand that. And I've also written a LinkedIn post about this which is about what is the reality of data analyst, right? Let me let me share that with you. So, here I clearly explain like who can land a job or who cannot land a job, right? So, if you are simply watching tutorials, enrolling in a course, and taking somebody's advice, and preparing, you are never going to land a job, right? Do not waste your efforts at least. Please know what is required to land a job. This is a very competitive field. You need to understand that and you have to do that extra thing to land a job. And we have also made a video on all these research things, right? This is again only to help you. So, there is a video we made which is about is data analyst a good career in 2026. So, you can watch this video, right? You can watch this video and you can check the comments of people, right? How they are finding it useful. You know, what people are telling about this video. You can watch this because this video is not based on what we think or what the reports say. We have also spoken to the industry people, people who are in the industry. We have spoken to them. We have gathered those insights. And on top of that, I've been in the data industry for about 10 years. Davel has been in the industry for more than 12 years. We have put all those insights together. And given this piece of information to you so that you can make your own decision. In this video we have not said something like oh, you must become a data analyst. It is going to give you a a big growth or something like that. Or we have not said like don't become a data analyst. Everything will be automated by AI. We did not take any extremes. We just understood what is happening in the industry and clarified our understanding with you. Okay? So, please watch this video if you have not before starting to prepare for the data analyst role, because it will actually give you the outcome for the effort that you are putting. Without understanding all these things, if you are just learning stuff, you will be wasting time. I really don't want you to do that. So, let's begin with the week one. And also know that you have to prepare your mind and body for this because it requires a certain focus and concentration. Do not do this, you know, if you if you feel like you cannot focus or you cannot concentrate, do not do this because uh what will happen is like you might spend like a week or two and after that you will feel like, "Oh, no, it's not for me." You know, you will find some reasons not to do that. Just prepare your mind first. Just accept that you're entering a very competitive field. This is not a field where like how people show in those Instagram videos where saying like, "Oh, just you can learn to build this dashboard in 5 minutes and you can earn 17 LPA, 18 LPA." All those are BS. Do not do not trust them, you know, they just do that to get money. They just don't want to uh you know, give the actual information, what is the real information in the industry. They just using the marketing gimmicks, right? For people who are finding this information new, you should also take this uh scam awareness course, right, that we have created in the past. Uh we created this 1 year back. So, this is a scam awareness course where we have clearly listed all the different kind of scams happening in the data industry and you need to go through it. If you if you're not aware, if you are someone who are watching this video for the first time without having any idea about the scams happening in the data industry, please watch this video. It will open your eyes. Okay? And uh coming back to preparing your mind and body, you need to use internet like internet. When I say internet, it's also about the AI and everything. You need to use that as your second brain. And you know, the job market is not very great across the world, not just in India. So, there will be some people who will have the victim mindset saying that, "Oh, you know, we won't get this. Those people are not helping me. The companies are like this. Companies are evil." Disengage from those kind of people because they always find an external reason to complain and not move forward in the life, right? Disengage from them and find people who have the hero mindset. When I say hero mindset people, so you might have seen in the movies, right? The the hero will have lot of difficulties. Uh amidst all those difficulties, the hero will emerge, right? That's when we clap for them. So, find those kind of people. I totally understand, right? The world is not great. Uh it is not very fair. Uh it is always not uh very uh reciprocative to your efforts. You put a lot of effort. The universe is not giving you back sometimes. But that doesn't mean you should simply complain about external factors and then and then, you know, uh drown yourself into demotivation. Don't do that. Just understand that you are required to make certain efforts and the outcome is something you can't really define because there are a lot of external factors. For you, what is important is that you make the effort and leave the outcome, right? Leave the outcome to the nature. One day you will find that all the efforts that you have made, once you have reached a certain spot, you will feel like, "Oh, I did learn a lot through that hard phase." So, that is very important. So, find the people who have that hero mindset and engage with them only. And uh eating properly, sleeping properly is very important. If you want to uh read any spiritual book, I would highly recommend it because it will align with your soul and your efforts will become very conducive to the outcome. All right, in the week one you can start with excellent business math and statistics. Uh all you have to understand is some basic formulas like sum, average, product, mean, uh all those basic things you need to understand. And uh you need to understand the advanced formulas like VLOOKUP, match, index, pivot tables, all those things, right? So, we have created a free video on this. So, you can go to this video. So, I'm just clicking this link and showing you. So, in this in this video you can see we have used a simple food data simple movie data set and made you understand all those Excel basics. This is good enough for you to start. And apart from that we have attached other resources for you to explore. If you're looking for something very structured, right? If you don't have time to go into multiple resources and if you're looking for structure, then only you can go for this paid course from CodeBasics. It is it is up to you. I mean, you can achieve the same outcome with the free courses as well, but the paid courses will help you in a way it is more structured and uh you will find a lot of exercises, business uh scenarios and those kind of things. Okay? And uh in terms of business math and statistics, very important thing you need to understand what is arithmetic maths, right? You need to understand what are percentages. Uh for example, right? When Apple is uh selling a phone, the revenue is growing from 50 to 60 million, what is the percentage of growth? So, you should be immediately able to answer the growth is 20% because companies talk in percentages. Nobody will say like, "Oh, we got like 10 million extra this year." Nobody will talk like that. Everybody will say, "Okay, how much percentage we have grown?" So, you really have to convert all your conversations in percentages. If you learn how to do that, this will naturally help you to succeed in the data analytics field. Okay? And uh again, we have attached all the free learning resources here, you know, there is one from Khan Academy. Uh it will teach you the basic maths and statistics. Everything is attached here. You can you can find all the links here. And uh then let's come to something very important. This roadmap is structured in a way where you are building your skills, but alongside, you need to know how to show those skills. Because in today's world, if you're only building skills but not knowing how to show them, you will not succeed. You will not knock that gate of a job. You will not do that. So, you need to know how to show those skills and LinkedIn is one of the platform to do that. So, you can use this LinkedIn checklist to polish or create your profile. If you have not created one, this is a free checklist and you can just click this link and download it. So, you can see it contains everything, right? Like how you have to keep your profile visibility, how you have to fix your profile picture, your uh banner image, your headlines. Every single thing is given here. All you have to do is just go through it, right? So, now you might wonder why LinkedIn is important. Why why to create a LinkedIn profile? Let me just help you with an example, right? Let's say you're selling something, right? In the right side you can see the image where you're selling it in a in a place where it is not uh marketplace, right? So, only few people will come. Of course, they will say, "Okay, uh the food is good, great." and all, but only few people can come. But let's say you are selling the same thing in a busy market where a lot of people come. Then what happens? Your business skyrockets, right? This is exactly what happens with LinkedIn. You have a skill and you are putting that in the marketplace for professionals. This is the biggest marketplace on planet Earth for professionals. You're putting your skills there and you're constantly doing something so that professionals will notice your profile and then your profile gets highlighted among them. So, that's the reason you need to have a very strong LinkedIn profile. And uh we have also included some section for your motivation where, you know, you can watch people who have achieved something extraordinary. I would take the story of Khushboo Rani who was a BSC fresher and she transformed to a data analyst. So, I've shared a LinkedIn post on her on her story. I've just attached the link to the same. So, you you can see this, right? Like from where she has started and uh she said how she did not have money to buy a laptop and then how she cracked the job role later. So, you can take these stories to get some inspiration. But the problem with this motivation is one thing. Uh don't rely on it, right? Just use motivation as this extra pump to to gain some momentum, but always trust on discipline because that is what will stay with you. So, discipline is such a beautiful thing because it will make you do something even on the days when you're not motivated. If [snorts] you get to the habit of discipline, then you have won everything, right? Like there is nothing that can stop you. There are days you're not motivated. There are days you're not feeling well, you know, you will still do it. That's unbelievable superpower. If you have got that, if you are building to that, then trust me like you don't have to worry about anything, right? That's that's something you should focus on while working on this roadmap. Okay? So, then there are some assignments which you must do because if you're not doing assignments, if you're not practicing, it's like watching swimming lessons. You're watching how to swim. Can you learn swimming by that? No, right? You need to get into the water. For those things we are giving you this assignment, so please make sure you are practicing them. You can also put it in LinkedIn and tag us and uh we are happy to engage uh on your post so that your profile gets more visibility, right? So, we are happy to do that for you. And uh so, the one insight I want to give you is that let's say 100 people have watched this video and picked this roadmap. Uh only 60 people would complete this stage, right? So, if you are completing this stage, just know that you are among the top 60. If you're completing this particular part, right? So, so all the best. And uh so, then comes the second leg which is week three, four, and five. So, here you can pick any of the BI tools, right? It can be Power BI, Tableau, Qlik. Do not worry about which tool you need to pick, right? Let's say you are already working and your company is using Qlik, then pick Qlik. If they are using Looker, pick Looker. If they're using Tableau, pick Tableau. But you are a fresher, you don't know which tool you will be using later, then pick Power BI because Power BI is the one, you know, leading in the Gartner's Magic Quadrant. So, if you don't know what is a Gartner Magic Quadrant, let me tell you. Let me show this link. So, Gartner is a research agency. They position the technology players within a specific market every year. And this is something what people look into, right? This is something what companies look into. They somehow want to get into the top of the Gartner Magic Quadrant. They really respect and revere the Gartner Magic Quadrant. So, just follow Gartner Magic Quadrant, what they are saying. So, for 2025, they They said Power BI is still the leader in the Gartner Magic Quadrant, right? So, that's why I said if you don't have a necessity to pick one tool, like let's say you don't have a necessity to only pick Tableau, then pick Power BI because most of the companies are switching to Power BI now. They are using Power BI. But, also don't worry if you pick Power BI and later you have to learn Tableau, it's not going to take a lot of effort because the fundamentals are the same. It's like you are learning to drive a Yamaha, you can also drive a Hero Honda, right? It's not going to be very different. Some changes will be there, but those are not going to take a lot of time. You can do transfer learning, okay? So, again, before starting this week, you have to research on the trends, what is unified analytics, what is Microsoft Fabric, what is Databricks. First of all, what is unified analytics? Data analytics and data engineering combining together, that is a unified analytics, that's a trend. You need to understand what is it. And what are the tools which are built on base of this trends, which is Microsoft Fabric and Databricks. You need to understand them as well, okay? And uh what is the edge for data analyst in the age of AI? All those things do a thorough research, right, before beginning this week. And we have also created a data analyst FAQ navigator, which you can access from here. So, basically this is comprising of all the questions that we get and the corresponding answers. So, let me share it with you. So, if you are a fresher, you can simply click fresher here. You can You can select the question and you will find an answer, right? You'll find an answer along with the key resources. This is done to make sure that you are not lost during the road map, right? During the road map, you might have multiple questions. Of course, you have to search the internet as well. This is just a consolidation of these questions and our answers, but you need to check outside as well. Like I said, doing a thorough research is very, very important, okay? I would also recommend you to join a community at this stage. There is There is an Indian Data Club. You can join this club because they do a lot of lot of activities. So, recently they do a lot of offline activities and online activities as well. So, they started something called Founders Walk where they are taking one founder from an industry and they are making them walk along with the aspirants and you can ask them questions during a walk. This is an amazing idea. And so, they recently they also conducted something called 21-day challenge where people from 22 countries participated, more than 29,000 participants. That's an amazing stuff. And they made 37 lakhs of lines of code written by these people. So, why participating in this kind of challenges and this kind of community is important? Because you find people who have the hero mindset, right? It's very easy for you to work along with people who have the hero mindset, who wants to improve, wants to challenge themselves, and move forward in the career. That is number one. And number two is this is the stage for you to build networking, right? So, let's say you start building the network from now on, right? You would meet some professionals, you would meet some fellow aspirants. It's very easy for you to ask for referral in the later stages or you can refer some people also, right? This way what happens is that you get an extra edge where there are a lot of applicants for a particular role. Now that you are part of the community, you already have some connection or some network with with professionals. It gives you that extra edge. That's the intention of creating this offline community, all right? And then with Power BI, the first thing you need to understand is like how to connect to different data sources, how to do data transformation in Power Query, creating a DAX metrics, data modeling, visuals, dashboarding. Using Power BI service is very important. So, these are the major things you have to understand and I've attached some free resources here. And I've also attached some YouTube channels to follow. You can follow them. And like I said, there is a track B with an affordable fee. There is a Power BI course. If you want to take that, you can explore that as well, all right? And I've already answered this question whether you have to learn Power BI, Tableau, or any BI tool. So, like I said, if you don't know which tool to pick, pick Power BI. You can transfer your learning to other tools later. It's It's going to be very easy. And in terms of people to follow, I have given list of some people who are regularly posting genuine stuff about data analytics. You can You can follow them. And one way to increase your profile visibility is you have created your LinkedIn profile, right? Now Now go to these people's posts and comment. Don't say great post, appreciate your insights. Don't comment generic stuff. Comment meaningfully. Say that share your thoughts. So, this way what will happen is like your profile gets appeared in their followers and their followers might be some professionals, right? So, this way your profile becomes more familiar and you can connect with fellow professionals as well because some people might like your comment or they can respond to your comment. So, this way what happens, right? You start building a connection, you start building a familiarity. You can go straight away to the person's inbox and say, "Hey, thanks for uh you know, responding to my comment. I think we both agree on this point." It's a good starting point to have a meaningful discussion, right? So, all this thing might look like a lot of effort, but my friend, trust me, this is what the world needs right now. You have to make that effort to get into this field and have a stable career. So, this is a stage you must understand the business fundamentals and domain knowledge. At first, try to understand the difference between domains and functions, right? I've given a link to that. Just follow that link, you would understand that. >> [snorts] >> Then, try to understand some business concepts from documentaries like Think School has some really good documentaries. You can go through that and understand that. And [snorts] the most important thing for you to understand is a profit and loss statement. You can simply go to, you know, Google and try a profit and a P&L statement, right? You would get something like this, right? You can just simply go through this. So, this is Amazon's profit and loss statement. So, I'm giving the link to this document. Try to understand this. What is the net revenue and what is the operating income, what are the cost involved, and what is the total profit, right? Revenue, cost, profit. These are the three major things. You will be working on those things irrespective of whatever job you do because at the end, the bigger picture is based on these things. And this is why I'm stressing that you need to understand the profit and loss statement. So, I'm attaching the link for the same. All right? And then a core skill is like outreach, how you need to reach out to people. So, I've given a couple of links here. The first link is like saying like how you can get help, right? Because a lot of people keep complaining that nobody helps me in office and things like that. This post will open that mindset for you that you don't need somebody all the time, right? Because if you go to companies, what really matters is like how much you are self-reliant, how much you are able to do stuff on your own. That r