Data Analytics Full Course 2026 | Data Analytics Tutorial | Data Analyst Course | Simplilearn

Key Takeaways

[Music] Hey there, welcome to SimplyLearn's data analytics full course. Data is the backbone of today's world and businesses rely on it to thrive. So whether you're just starting out or looking to upgrade your skills, data analytics is no longer just a nice to have. It's a gamecher and one of the hottest skills in the job market right now. But here's the best part. This course goes beyond traditional analytics. Now with the rise of generative AI, data analysis is becoming faster, smarter, and more powerful than ever before. So in this course, we will keep it real and hands-on. First, you'll get a strong grasp on the fundamentals of data analytics while exploring practical real world application. Then we'll guide you through the process of collecting, cleaning, and visualizing data. And to take you into the world of predictive analytics, you will also work with industry-leading tools like Excel, PowerBI, Python, SQL, R, and even Chad GPT. That's right. You'll see firsthand how Genai tools like Chad GPT are transforming the role of analyst by automating task and speeding up insights. With exciting projects and toolbased training, by the end of this course, you'll not only understand analytics, you'll be equipped to harness the power of generative AI in your work. Making smarter decisions that have a real impact. So, let's get started. Also, if you're interested to supercharge your career in data analytics, the professional certificate program in data analytics and generative AI by ENICT Academy, IT Goi is the perfect choice for you. This is an 11-month live online course offered interactive master classes by IIT Gojhati faculty and industry expert combining cutting edge tools like generative AI, charge GPT, Python, Tableau and SQL. You'll get to experience hands-on learning with real world projects and immerse yourself in the campus environment with a special session at IIT Gojhati. Plus, earn an executive aluminiz status from IT Gojhati and IBM recognized certifications to stand out in the job market. And with practical training, career assistance and curriculum tailored to today's industry needs, this program is your pathway to become a data analytics expert. So, what are you waiting for? Hurry up and enroll now and you can find the course link below. >> In this video, we will discuss what data analytics is and the need for data analytics. Then we will look at the different ways in which data analytics can be used followed by the various steps involved in the data analytics process. After that we will get an idea about the different tools used in data analytics and the companies using data analytics. Moving forward we will see a case study on how Walmart uses data analytics for better customer service. And finally we will perform a regression analysis in R to predict sales based on the advertising expenditure from three mediums TV ads, radio ads and newspaper advertisements. So what is data analytics? Companies around the world are generating vast volumes of data every hour. This data could be in the form of log files, web server and transactional data as well as various customer related data. Also, data is being generated at a rapid rate from social media websites and applications such as Facebook, Instagram, Twitter and WhatsApp. Companies want to use this data to derive value out of it and make business decisions. That's where data analytics comes into use. Data analytics is the process of exploring and analyzing large data sets to find hidden patterns, unseen trends, discover correlations, and valuable insights to make business predictions. Data analytics improves the speed and efficiency of your business. A few years ago, a business would have gathered information manually, performed statistical and complex analytics, and unearthed information that could be used for future decisions. But today that business can identify insights on the fly for immediate decisions. Most organizations have big data and many understand the need to harness that data and extract value out of it. So they use a lot of modern tools and technologies to perform data analytics. Some of the tools I will discuss in detail later in this tutorial. Now that we have looked at at what data analytics really is, let us understand the ways in which you can use data analytics. First is improved decision making. Data analytics eliminates a lot of guesswork and manual tasks from choosing the right content, planning marketing campaigns and developing products. Organizations can use the insights they gain from data analytics to make informed decisions leading to better outcomes and customer satisfaction. It gives you a 360deree view of your customers which helps you understand their behavior completely enabling you to better meet their needs. Second is better customer service. Data analytics provides you with more accurate insights of your customers, allowing you to tailor customer service to their needs, provide more personalization, and build stronger relationships with them. Your data can reveal information about your customers communication preferences, their interests, their concerns, and more. It helps you give better recommendations for products and services. Next is efficient operations. Data analytics can help you streamline your processes, save money, and boost production. When you have an improved understanding of what your audience wants, you waste less time in creating ads and content that don't match your audience interests. This helps you optimize your campaigns, create better content strategies, and hence improve results. And finally, we have effective marketing. When you understand your audience better, you can market to them more effectively. Data analytics also gives you useful insights into how your campaigns are performing so that you can fine-tune them for optimal outcomes. You also find out the probable customers who are the most likely to interact with the campaign and convert into leads. Now let's discuss the various steps involved in the data analytics process. As you can see on the screen, there are five process steps. Now let me make you understand each of this one by one. So the first step is to understand the problem. Before starting with the analysis, you need to understand the business problem and define your goals. Asking questions at the outlet is vital because this would address issues such as how can we reduce production costs without sacrificing quality? What are some of the ways to increase sales opportunities with our current resources? Do customers view your brand in a favorable way? Answers to these questions will help you build a clear road map with lucrative solutions. Also try to find out the key performance indicators and consider the metrics to track along the way. The second step in the process is data collection. After you have finalized your goals, it's time to start looking for your data. Data collection is the process of gathering information on targeted variables identified as data requirements. The emphasis is on ensuring accurate and right data is collected. Data collection starts with primary sources which are also known as internal sources. This is typically structured data gathered from CRM software, ERP systems, marketing automation tools and others. These sources contain information about customers, finances, gaps in sales, etc. Under external sources, you have both structured and unstructured data. So, if you're looking to perform a sentiment analysis towards your brand, you would gather data from various review websites or social media apps. The next step is to clean the data. The data which is collected from various sources is highly likely to contain incomplete, duplicate, and missing values. So you need to clean these unwanted redundant data to make it ready for analysis. So to generate accurate results, analytics professionals must identify duplicate and anomalous data and other inconsistencies that could skew the analysis. According to a report, 60% of data scientists say most of the time is spent cleaning the data while 57% of data scientists say it's their least enjoyable task. Now the fourth step in the process is data exploration and analysis. Once data is cleaned and ready, you can go ahead and explore the data using data visualization and business intelligence tools. You can also use various data mining and predictive modeling techniques to analyze the data and build models. You can use different supervised and unsupervised algorithms such as linear regression, logistic regression, decision tree, KN&N, C means clustering and lots more to build prediction models for making business decisions. And the final step is to interpret the results. This part is important because it's how a business will gain actual value from the previous four steps. Interpreting the results will help you find unseen trends and patterns in the data and gain insights. You can have a validation check if the results are answering your questions. These results can be shown to your clients and stakeholders for better understanding and business collaboration. Now that we have looked at the various steps involved in data analytics, let's now see the different tools that can be used to perform the above steps. So as you can see we have seven tools including a few programming languages that will help you perform analytics better. Now let's discuss them one by one. First we have Python. Python is an object-oriented open-source programming language that supports a range of libraries for data manipulation, data visualization, and data modeling. Python programmers have developed tons of free and open-source libraries that you can use. You can find many of them via the Python package index which is py the repository of Python software. Python provides the default package installer called pip or pip. Python has libraries such as numpy for numerical computation of data. Pandas to manipulate data on numerical tables and time series. Then you have scypi for technical and scientific computations. It also provides scikitlearn which is a machine learning library for creating classification, regression and clustering algorithms. And finally, it also has PyTorch and TensorFlow for deep learning. Up next, we have R. R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data analysis and visualization. Some of these libraries are ggplot, tidyiverse, plotly, deplier and carrot. Then we have Tableau. Tableau is a popular data visualization and analytics tools that helps you create a range of visualizations to interactively present the data, build reports and dashboards to showcase insights and trends. It can connect with multiple data sources and give hidden business insights and patterns. Then we have a competitor of Tableau which is PowerBI. PowerBI is a business intelligence tool developed by Microsoft that has an easy drag and drop functionality and supports multiple data sources with features that make data visually appealing. PowerBI supports features that help you ask questions to your data and get immediate insights. You can also forecast your data for predicting future trends. So the next tool is click view. Click view provides interactive analytics with in-memory storage technology to analyze vast volumes of data and use data discoveries to support decision- making. It provides social media discovery and interactive guided analytics. It can manipulate huge data sets instantly with accuracy. Up next we have Apache Spark. Apache Spark is an open source data analytics engine to process data in real time and carry out complex analytics using SQL queries and machine learning algorithms. It supports Spark streaming for real-time analytics and SparkSQL for writing SQL queries. It also has Spark MLIB which is a library that has a repository of machine learning algorithms and then it has graphics for graphical computation. And finally we have SAS. SAS is a statistical analysis software that can help you perform analytics, visualize your data, write SQL queries, perform statistical analysis, and build machine learning models to make future predictions. SAS empowers our customers to move the world forward by transforming data into intelligence. SAS is investing a lot to drive software innovation for analytics. Gartner has positioned SAS as a magic quadrant leader for data science and machine learning. Moving on to the applications of data analytics. Data analytics is being used in almost every sector of business these days. Let's discuss a few of them. First, we have retail. Customers expect retailers to understand exactly what they need and when they need it. Data analytics helps retailers meet those demands. Retailers not only have an in-depth understanding of their customers, but they can also predict trends, recommend new products, and boost profitability. Retailers create assortments based on customer preferences. Invoke the most relevant engagement strategy for each customer. Optimize supply chain and retail operations at every step of the customer journey. The second application is on healthcare. Healthcare industries analyze patient data to provide life-saving diagnosis and treatment options. They also deal with healthcare plans and insurance information to drive key insights. Using analytics, they can discover new drugs and come up with new drug development methods. Advanced analytics allows healthcare companies to improve patient outcomes and experience. Cancer cells and diabetic retinopathy can be discovered using medical imaging. At number three, we have manufacturing. For manufacturers, solving problem is nothing new. They fight with difficult problems and situations on a daily basis. From complex supply chains to motion applications to labor constraints and equipment breakdowns, they deal with such problems on a regular basis. Using data analytics, manufacturing sectors can discover new cost-saving and revenue opportunities. The fourth application is related to the banking sector. Banking and financial institutions collect vast volumes of structured and unstructured data to derive analytical insights and make sound financial decisions. Using analytics, they can find out probable loan defaulters, customer turnout rate and detect fraudulent transactions immediately. The final application is based on logistics. Logistics companies use data analytics to develop new business models that can ease their business and improve productivity. They can optimize routes to ensure delivery reaches on time in a costefficient manner. They also focus on improving order processing capabilities as well as performance management. With that, now let's look at the companies using data analytics on a daily basis. So we have the e-commerce giant Amazon. Then we have Axenture. Followed by the American healthcare service organization Sigma. Then we have the American supplier of health information technology solutions services devices and hardware Cerner followed by Target and antivirus company Mac Cafe. Next we have Rapido which is an Indian bike rental company based in Bangalore. After that we have Flipkart and the world's largest retail company Walmart. With that let's understand a case study from Walmart and how it uses data analytics to grow its business and serve its customers better. Walmart is an American multinational retail company that has over 11,500 stores in 27 countries worldwide and it has e-commerce websites in 10 different countries. It has more than 5,900 retail units operating outside the United States with 55 banners in 26 countries with more than 7 lakh associates serving more than 100 million customers every week. It has over 2.2 million employees around the world and 1.5 million employees in the United States alone. Walmart's e-commerce branch alone employs more than 3,000 technologists from Silicon Valley to India, England, and South America. More than 240 million customers shop at Walmart each week online and at its banner stores. Walmart.com sees up to 100 million unique visitors a month according to com score and is growing every year. Walmart collects over 2.5 pabytes of data from 1 million customers every hour. That's really huge. Now, to make sense of all this information, Walmart has created Data Cafe, a state-of-the-art analytics hub located within its Bantonville, Arkansas headquarters. Here over 200 streams of internal and external data including 40 pabytes of recent transactional data can be modeled, manipulated and visualized. Teams from any part of the business are invited to bring their problems to the analytics experts and then see a solution appear before their eyes on the nerve centers touchscreen smartboards. Walmart also constantly analyzes over 100 million keywords to know what people near each store are saying on social media to understand the customer behavior on what they like and dislike. Walmart uses modern tools and technologies to derive business insights and improve customer satisfaction. Some of these tools include Python, SAS, NoSQL databases such as Cassandra and Hadoop. Now using all these technologies and data analysis techniques, Walmart can better manage its supply chain, optimize product assortment, personalize the shopping experience, give relevant product recommendations, and finally optimize and analyze transportation lanes and routes for its fleet of trucks. With that, let's jump into our use case demo where we will predict the sales based on advertising expenditure using the linear regression model in R. The advertising expenditure has been made via different mediums such as radio, television, and newspaper. We will use the R programming software to implement the demo. So, why R? Well, R is a free and open-source software that can be downloaded from the RARN website. It is easy to learn and use. Our language is built specifically for performing statistical analysis, data manipulation, and data mining using packages such as plier, dlier, tidier, and lubricate. R supports data visualization with the help of packages such as ggplot, Google, r colorer, leaflet, and gg map. And finally, the R software can be used in a wide range of analytical modeling including classical statistical tests, linear and nonlinear modeling, data clustering, time series analysis, and more. Now, let's have a look at our data that we will be using for this demo. Here is our advertising CSV data set which has four columns. You can see there's TV ads expenditure. The next column is for radio ads. Then we have the newspaper ads and the last column is our target column that is the sales. So the data set has in total 200 rows. Now to understand the data, let me give an example. So consider the second row. So suppose you spend around $230 on TV ads, then $37.8 on radio ads and $69.2 newspaper ads, you can expect to sell nearly 22 units of a particular product. Similarly, if you are spending $44.5 in TV advertising, $39.3 in radio ads and $45 in newspaper ads, you can sell around 10 units of certain item. We will analyze this data using linear regression. So linear regression is a supervised learning algorithm which means the data has labeled columns and is used to predict numeric continuous variables. So our sales column here is the target column and it has continuous numeric variables. Now let me go to the R studio and start with the demo. So first I'll create a new file. Then I'll select R script. The next step is to install all the necessary packages that we need for this demo. If you already have the packages installed in your R studio, you need not do it again. You can just call these packages using the library function and pass the package names. So first I will install the dlier package which is used for data manipulation. I'll be using install.packages function and I'll give the package name. So I'll type install dot packages. If you hit tab it will autocomplete. Then under quotations I'll write dlier. I'm not going to run this because I already have it installed in my R studio. The next step I'll write I'll call this uh package using library function. I'll give the package name deplier. I'll run this. Then I'll install the broom package. It takes the messy output of built-in functions in R such as linear model or LM then t test and turns them into a tidy data frame. So I'll copy the above code. I'll just paste it again. I'll change it to broom. Here also I'll change it to broom. I'll run this. Okay, then I'll be installing the CA tools package which will help us build our linear regression model. I'll paste the same code and I'll take CA tools and I'll call that using the library function. I'll run this. Now sometimes people face issues with installing this particular package. If you also face this problem, do visit the R Studio community page. Now let me show it to you. So this is the R studio community page and here they have the solution. You can just go through this two pages. All right. After this, I will install the ggplot2 package, which is a very popular package in R for data visualization. I'm not running install.packages because I have already installed all these packages before. If you have not, so you have to run install.packages first and then call the library function. With that, let's now load the data set. For this, I will use the read.csv function and provide the path location where my data is located, followed by the data set name and the extension. I'll assign the loaded data set to a variable. Let me now go ahead and show you where my data set is located. So here is my advertising CSV data set and this is the location. I'll copy this location. I'll move back to R Studio and let me comment this line. Load the data set. So I'll take a variable name adds and then using read dot CSV function I'll pass the path location where the data set is present. Now one thing to note is we have to change all the backslash to forward slash otherwise R won't accept it. And finally I'll give the data set name which is advertising dot CSV. Let me run it. Okay, we have successfully loaded our data set. Now let us look at how our data set looks like using the head function. So I'll give a comment display the head of the data set. I'll be using the head function and I'll pass ads. Run it. So you can see the head function has displayed the first six rows from the advertising data set. Let me now check the dimensions of the data set. So I'll use the dim function. Uh it will give you the total rows and columns present in the data set. Give a comment. Check the dimensions. I'll use the dim function and I'll pass in the ads variable. You can see it has given the number of rows which is 200 and the total columns which is four. Now if you want to get a summary of the data set you can use the summary function. So I'll directly type in summary and I'll give ads. Let me expand this. So actually summary function gives you information about a few statistics for each of the columns. So you can see the minimum value for each column, the maximum value for each column, the mean, the median, first quartile and the third quartile values. The first quartile or lower quartile is the value that cuts off the first 25% of the data when it is sorted in ascending order. The second quartile is the median which has the value that cuts off the first 50%. And the third quartile or the upper quartile is the value that cuts off the first 75% of data. Moving ahead, let's do some data visualization now to visualize our data. Since our data has only numeric values, using scatter plots would be the best option. So we will visualize our sales against each of the independent variables. For that I will use the plot function and give sales in my x-axis and the independent variable names in the y-axis. Let me now do that. So I'll give a comment data visualization. First I'll use the plot function and then in x-axis using the dollar symbol I'll give sales. Then in the y-axis I'll give my independent variable. You can see r is automatically giving you the suggestions. I'll select TV. Then I'll take type is equal to under quotes I'll give P which stands for points and I'll take the color as red. So you can see under plots we have our scatter plot. If I zoom in, you can see the red dots are pretty much aligned in one direction, which means if you are increasing the expenditure on TV ads, the units sold are also increasing equally. So the more you spend on TV ads, the more sales you can expect. I close it. Now let's look at how sales vary based on radio advertising expenditure. I'll copy this, paste it and under yaxis I'll change it to radio and I'll take the color now as let's say blue color. I'll run it. Now if I to zoom in now if you look at the blue dots it is not that linear compared to our previous graph. You can see there are a few data points like this that show the sales were not good even after spending decent money on radio ads. But still you can expect a decent amount of sales if you are willing to spend on radio advertising. Close it. Let's now look at how sales will vary based on the newspaper advertising expenditure. I'll change the radio to newspaper column and this time I'll take color as green. I'll run it. Let me zoom in. You can see the plots are very hapazardly present. The data is completely nonlinear and there seems to be a low correlation between the sales and newspaper advertising expenditure. Now, if you want to look at these plots at a time, you can use the pairs function. So, I'll type pairs and then pass in my variable name, which is ads. I'll run it. Let me zoom in. So, this is our plot and you can see this has all the visualizations. So you can see the TV sales. Now you can see the sales that were made with radio expenditure and with the newspaper expenditure as well. I'll close it. Moving ahead. Let's check the correlation between the variables and see what inside we can get. We will use the core function or co function and build a correlation matrix. First let me go ahead and install the core plot package. So I'll give a comment correlation analysis. For this I will have to install the core plot package. I've already got it installed. Then I'll call this function using library. I'll run it. You can see core plot the version has been uploaded. Now I'll tell you how you can grab only the numeric columns. Now our data only has numeric columns but still let me tell you how you can do it. Since correlations are based on numeric columns only. This can be done using the s apply function. So for that we have already installed the the dlier library. I'll give a variable name as num dot calls which is numeric columns. Then I'll pass in the apply function. I'll give the ads variable and I'll check if the variable is numeric or not. So I'll use is dot numeric. Let me run it. And now let's display what's there in num dot calls. You can see it says TV it's true which means TV has numeric values. Even radio has numeric values. Similarly for newspaper and sales also. Then I'll use the correlation function which is co to display the correlations between the variables. So I'll give my variable name as C do data and then I'll take the core function pass in the ads variable and I'll only filter out the numeric columns. So comma numeric columns means we need all the rows and the selected columns. Let me run it. And now to display let me call CO data again. So this is our correlation output. As you can see the correlation values are all above zero which means there is a positive correlation between the variables and the change in one of the independent variables will have a positive impact on the sales numbers. TV ads have the maximum correlation with sales and the value is around 78. Then there is radio advertising which has correlation of about.57 with sales and newspaper ads have the lowest correlation compared to the other two which is at 22. Now you can also build a correlation matrix using the correlation plot method. This will give you a visual representation of the correlation between the variables. So let's see how we can do that. I'll type core plot and I'll give code data and I'll pass a method as color. If I zoom in, you can see this is our correlation matrix. On the right, you can see the scale. So minus1 is for negative correlation. Then there's light red, zero, which is almost white color. Then there's light blue. And finally dark blue for the maximum positive correlation. The diagonals are dark blue which represents the same variables as in rows and in columns. So it's dark blue. TV ads and radio ads have the next highest correlation while newspaper ads have the lowest correlation with sales. With that, let's jump into the most important part of this analysis, which is building our regression model. First, we will look at a simple linear regression model where we will take one input variable that is TV ads. I'll be using the lm function or the linear model function to build the model. So I'll give a comment simple linear regression I'll take a variable name as model simple and then using lm function I'll give my target variable which is sales and using tild I'll give my independent variable which is EV and data as ads. I'll run it. Now that we have built a linear regression model, let's check the summary. Take summary function and I'll pass in model simple. Let me run it. Now if I expand this you can see our intercept estimate is around 7.03. So when the TV advertising budget is zero we can expect sales to be around 7,30 or 7030. Also remember we are operating in units of thousand and for every $1,000 increase in the TV advertising budget we can expect the average increase in sales to be around 47 units. Now the same summary can be checked using the tidy function present in the broom package. So if I call tidy and I'll give the model name which is model simple. I'll run it. So there you go. This gives us a tidy representation of the summary figures. Now let's build a regression model with more than one input variable. So we'll build a multiple linear regression model. I'll take my variable name as model multiple this time and I'll use the same lm function. I'll pass in the sales and using till I'll take all the column names TV then I'll use an addition operator then I'll take in newspaper followed by the radio column and then I'll take my data as ads. Let's run it. I'll follow the same drill. Let me now call the summary function over this newly created model. So I'll write summary and I'll select my model name as model multiple. Let me run it. So the interpretation of our coefficients is the same as in simple linear regression model. First we see that our coefficients for TV and radio advertising budget are statistically significant since our P value is less than 05 while the coefficient of newspaper is not which is around 86. Thus, changes in the newspaper budget does not appear to have any relationship with changes in sales. However, for TV ads, our coefficient suggests that for every $1,000 increase in TV advertising budget, holding any other predictors constant, we can expect an increase in sales of 45 units on average. Similarly, the radio coefficient suggests that for every $1,000 increase in radio advertising, holding all the other predictors constant, we can expect an increase of 188 sales units on an average. Now, you can also call the tidy function over this multiple linear regression model. So, let me do that. I'll call tidy and I'll pass in model multiple You can see it has given the output. Now you can also find the coefficients of the model using another method. It's called the coefficient matrix. Here is how you can do that. So take a variable name and I'll use the summary function. I'll pass in model multiple and using the dollar symbol I'll take the parameter as coefficient. Let me call C coefficient now. So these are the coefficients of different variables. Let me now show you another example of how you can train a linear regression model using the CA tools library. First I'll take a seed value a random seed value of say 101. Next I will split the data into training and testing sets. I'll take 70% for training the data and 30% for testing the data. So I'll use a variable sample. Then I'll call sample.split. split take ads and then I'll use another parameter called split ratio and I'll take the split ratio as.7 which is 70%. I'll run it and then I'll use another variable called train and take the subset of the sample. Pass in my ads variable and I'll select sample is equal to equal to true. Similarly, I'll take another variable called test. I'll use my subset function and given the same parameters, but this time I'll take sample is equal to equal to false which means the test sample data set won't have any values that are present in train data set. I'll run it. Now we will use the same lm function to create our model. So I'll take model as my variable and assign it to lm function. So I'll assign the linear model to the model variable. I'll take sales as my target column. use the tild followed by a dot which means I'm taking all the variables in terms of the independent variables and then I'll select my train data set with that let's check the summary as well so this is the summary of our newly created model. Now you can also check the residual collector from the trained model using the residuals function. Let me go ahead and assign a variable called for residual and I'll use the residuals function. Pass in my model. Then I'll convert the residuals into a data frame. So I'll use the as dot data frame function and pass in. You can check the residuals. So these are the residual values. Now it's time to make our predictions using the test data set. I'll use the predict function for this. Let me take another variable called sales. predictions and I'll use the predict function. Pass in my model followed by the test data set. Now I'll run it. Then let me call sales.prediction to display the values. As you can see these are my predicted sales values. Now let me combine these predicted sales values to our original sales for the test data. For that I'll use the cbind function and pass the column names. I'll take another variable called results and use the cbind function. I'll take sales dot predictions and I'll consider the sales column from the test data. Let me check the values now. So you have the predicted sales values and the original values of sales. But you can see the columns don't have any name assigned to them. So let me go ahead and assign the column names using the call names function and convert it into a data frame to make it look better. So I'll use the call names function and and pass in my results variable. Then I'll take a vector and give the column names as spread for predicted values and let's say real for the original values. Me run it now. So I'll convert this into a data frame. So I'll use as dot data frame and give my results variable. Now if I display results you can see go on top you can see the columns have been assigned successfully. So on the left you have the predicted values and on the right you have the real values. So we have successfully built our linear regression model and predicted the sales values using linear regression in R. You can also go ahead and find the accuracy of this model to know how good your model is. We won't be covering that as part of this tutorial. I'll leave it for you and encourage you to do some research on how you can find the accuracy of a linear regression model. You will come across terms such as mean squared error, root mean square error and R squar value. If you are able to find the accuracy, please post the results in the comment section or if you face any issues with it, please post your queries. We'll be happy to help you. If you categorize the steps to become a data analyst, these are the ones. Firstly, you need to focus on skills. Followed by that you need to have a proper qualification. Then test your skills by creating a personal project, an individual project. Followed by that you must focus on building your own portfolio to describe your caliber to your recruiters and then target to the entry-level jobs or internships to get exposure to the real world data problems. So these are the five important steps. Now let's begin with the step one that is skills. So skills are basically categorized into six steps. Data cleaning, data analysis, data visualization, problem solving, soft skills and domain knowledge. So these are the tools Excel, MySQL, R programming language, Python programming language, some data visualization tools like Tableau, PowerBI. And next comes the problem solving. So these are basically the soft skill parts. problem solving skills, domain knowledge in the domain in which you're working maybe a pharma domain, maybe a banking sector, maybe automobile domain, etc. And lastly, you need to be a good team player so that you can actively work along with the team and solve the problem collaboratively. Now, let's move ahead and discuss each and every one of these in a bit more detail. Starting with Microsoft Excel. While advanced tools are prevalent, proficiency in Excel remains vital for data analysts. Excel versatility in data manipulation, visualization and modeling is unmatched. It serves as a foundational tool for initial data exploration and basic analysis. Data management. Database management skill is indispensable for data analysts. As data volume saw, efficient management and retrieval from databases is critical. Proficiency in database systems and querying languages like SQL ensures analysts can access and manipulate data seamlessly. Followed by that we have statistical analysis. Statistical analysis allow analysts to uncover hidden trends, patterns and correlationships within data facilitating evidence-based decision making. It empowers analysts to identify the significance of findings, validate hypothesis and make reliable predictions. Next after that we have programming languages. Proficiency in programming languages like Python is essential for data analysts. These languages enable data manipulation, advanced statistical analysis and machine learning implementations. Next comes data storytelling or also known as data visualizations. Data storytelling skill is paramount for data analyst. Data storytelling bridges the gap between data analysis and actionable insights, ensuring that the value of data is fully realized in a world where datadriven communication is central to business success. Data visualization skill is a corner store for data analyst. As data complexity grows, the ability to present insights clearly and persuasively is paramount. Next is managing your customers and problem solving. Managing all your customers data and companies relationships is paramount. Strong problem solving skills are important for data analyst. With complex data challenges and evolving analytical methodologies, analysts must excel in identifying issues, formulating hypothesis, and devising innovative solutions. In addition to the technical skills, data analysts in 2025 will require strong soft skills to excel in their roles. Here are the top ones. Data analysts must effectively communicate their findings to both technical and non-technical stakeholders. This includes presenting complex data in a clear and understandable manner. Next soft skill is teamwork and collaboration. Data analysts often work with multidisciplinary teams alongside data scientists, data engineers, business professionals. Collaborative skills are essential for sharing insights, brainstorming solutions and working cohesively towards common goals. And last but not least, domain knowledge. Knowledge on domain in which you're currently working is really important. It might be a pharmical domain. It can be an automobile domain. It can be banking sector and much more. Unless you have a basic foundational domain knowledge, you cannot continue in that domain with accurate results. Now the next step which was about the qualification to become a data analyst. Mast's courses, online courses and boot camps provide strong structured learning that helps you gain in-depth knowledge and specialized skills in data analysis. Masters programs offer comprehensive academically requested training and often include research projects making sure you're highly competitive in the job market. Online courses allow flexibility to learn at your own pace while covering essential topics and boat capability keeping you updated on industry trends and make you more attractive to potential employers. If you are looking for a well curated allrounder then we have got you covered. Simply learn offers a wide range of courses on data science and data analytics starting from masters professional certifications to postgraduations and boot camps from globally reputed and recognized universities. For more details check out the links in the description box below and comment section. Now proceeding ahead we have the projects for data analyst. Data analyst projects demonstrate practical skills in data cleaning, visualization, and analysis. They help build a portfolio showcasing your expertise and problem solving abilities. Projects provide hands-on experience bridging the gap between theory and real world application. They show domain knowledge making you more appealing to employees in specific industries. Projects enhance your confidence and prepare you to discuss real world challenges in interviews. Proceeding ahead, the next step is about the portfolio for data analysts. A portfolio is a testament that demonstrates your skill and expertise through real world projects, showcasing your ability to analyze and interpret data effectively. It provides tangible proof of your capabilities making you stand out to the employers. Additionally, it highlights your domain knowledge and problem solving skills, giving you a competitive edge during job applications and interviews. Last but not the least, data analyst internships. Internships provide hands-on experience with real world data sets, tools, and workflows, bridging the gap between theory, knowledge, and practical application. They offer exposure to industry practices, helping you understand how data is used to drive decisions. Internships also build your professional network, enhance your resume, and improve chances of securing a full-time data analyst role. >> Hello and welcome. Welcome to data analytics with R from simply learn. So are you excited to begin with today's session? Yes, you are. Okay, great. I'm excited as well to be a part of this session. What are we going to cover in today's session? We will cover couple of topics. We will begin with understanding the basics. We will focus on why data analytics and the importance of data analytics and then what is data analytics. We will also focus on understanding the life cycle of data analytics and I'll also try to give you some realtime examples so that we can relate these concepts with the realtime examples or scenarios and then we will understand the types of analytics and what do they mean. We will also focus on the benefits of using R. For this session, we will be using R studio tool. Hence, we have to understand why we use R for data analysis. And finally, we will perform some hands-on exercise. In today's session, we will only focus on how to import the data, how to structure the data, and also how to create some beautiful visualizations by using R studio. All right. Python and R are top programming languages that are in demand in data science community. Both have their own pros and cons. Therefore, it is vital that you understand the crucial differences between the two programming languages and decide which one is the best fit for you. After watching this video, you will understand which language is better for you in terms of different parameters. Before unwrapping today's topic, I request you to subscribe to our channel and press the bell icon to never miss any updates from Simply Learner and also get notified every time you get a similar video. Now let us look at what are we covering today. We are covering introduction to R and Python. Parameters in R and Python. Best tools and libraries offered by Python and R and conclusion to the topic. Introduction to R and Python. Let me ask few queries regarding Python and R. Our language is super superficially related to Python, C, C++, Java. Please leave your answer in the comment section below and stay tuned to get the answer. And another question is which one of the following is a Python file extension? P dot Python py and pyt. Please leave your answer in the comment section below. Coming to introduction to Python and R. R is a statistical programming language and environment that integrates statistical computing and graphics. R is powerful and stable software. Python Python can also be called as a generalpurpose programming language for data analysis and scientific computing. Python can be considered as the best player in machine learning. Python is an expressive language with many built-in function. Both are open-source software and platform independent and they are platform neutral and also compatible with all major operating systems including Unix, Windows and Mac. Next we will be covering different parameters. We will be covering learning preferability, mathematical fundamentals, speed of both languages, visualization and graphics, data handling capacity, demand, community and customer support, employment possibility in both the languages. Let us cover it one by one. First one is learning preferability or ease of learning. Python is renown for its ease of use. Python's notebooks offer excellent tools for sharing and documentation despite the fact that there are currently no GUIs for them. Programmers find R as difficult language as a beginner. This implies that the programmers must devote a significant amount of time to learn and comprehending are coding. Coming to mathematical fundamentals required. Coming to Python, understanding descriptive analysis is very important. In layman's terms, descriptive statistics often refers to the process of explaining using certain representative techniques such as charts, tables, Excel files, etc. Python statistics is a built-in library for descriptive statistics. If your data sets are not too big or if you can't rely on importing other libraries, you can use Python. On the other hand, R requires basic statistics. From basic statist statistics, what I mean is mean, mode, and median are the terms used most frequently in basic statistics. It is referred to as measures of central tendency. Probability statistics plays an important role in handling various types of probability distribution. It includes binomial and normal distribution. Next parameter is speed. Python is an interpreted language with dynamic typing. Python always executes slowly because the code is executed line by line. Compared to MATLAB and Python, RSR language is significantly slower. Our packages are substantially slower than those for other languages. Now that we have covered speed, coming to data visualization and data collection in Python. When selecting data analysis tools with visualization are crucial and Python has some incredible visualization tool in Python to large and varied scatter plots using regression lines we can use ggplot 2 and ggplot tools compared to raw values visualized data is easier to comprehend. Therefore, R has many packages that offers sophisticated graphic features. In R we can use in R we can use tools like M plot lip sabon etc. Data handling capability in both Python and R. The new releases in Python have resolved the issue with the Python packages for data analysis. R is useful for analysis because of the abundance of packages, accessibility of the test and benefit of employing formulas. However, simple data analysis can also be done using it the need to install many packages. Crucial part of parameter that is tools and libraries in Python and R. As a Python developer, one needs to be wellversed in the best libraries because Python has a lot of libraries that have many different uses. Libraries like TensorFlow, Scikitler, NumPy plays an important role in solving many Python related problems. Libraries perform a wide variety of task in R that are very beneficial for data science operations. Example for that is depier, bioonductor etc. Community and customer support support index offered by Python and R. Compared to R, Python has a larger community. For assistance, we can contact www.python.org. For any queries regarding Python and help you can support uh you I repeat you can visit support.realpython.com. For any help and queries R offers you with R studio community. R provides assistance through its official website. For queries and community related issues we can contact www.heenpro.org. or next is job opportunities in Python and R. A recent survey from indeed.com predicts that at least 55,000 Python jobs in the USA with exponential pay rates are available. Big tech companies like Google, Amazon, Twitter, Facebook requires Python developer to handle massive amount of data. Position provided for a Python developer is software engineer, data analyst, data scientist and many more. Career in R is an excellent job opportunity for you as a beginner. Big tech companies like Google, Twitter, Facebook are using R. Position provided by companies as a R developer is data scientist, data analyst, data visualization analyst etc. Moving on, let us wrap up an important topic which language to be used between R and Python. There is no right or wrong way to study both Python or R. Both are in demand skills that will enable you to complete almost any data analytics work you come across