Learn Data Analytics Online: Free Course with AI Tutor & Certificate
You'll learn to clean and visualize data using Python, SQL, and Excel. This free data analyst course online spans 24 modules and about 34 hours of video lessons. By the end, you can build predictive models and analyze large datasets to make smart business decisions.
What you'll learn in this Data Analytics course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Understand the fundamentals of data analytics and its role in various industries.
Apply data preparation and cleaning techniques to ensure data quality and integrity.
Perform statistical analysis and hypothesis testing to derive meaningful insights from data.
Utilize exploratory data analysis techniques to visualize and interpret relationships and patterns within data.
Apply predictive analytics and machine learning algorithms to make data-driven predictions and decisions.
Data Analytics course syllabus
01 Introduction to Data Analytics
- Understanding Data Analytics
- Why You Should Learn Data Analytics?
- Is Data Analysis a Good Career Choice?
- Essential Data Analytics Trends to Watch
- Five Key Skills to Become an Exceptional Data Analyst
- The Critical Role of a Data Analyst
- The Importance of Data in Business Strategy
02 Basics of Data Analytics
- Different Types of Data Analytics
- The Data Analytics Lifecycle Explained
- Understanding Qualitative and Quantitative Data Types
- Exploring Various Data Sources
- The Importance of Data Quality
- An Introduction to Data Warehousing
- Fundamentals of Data Mining
03 Data Collection and Data Wrangling
- An Overview of Data Collection Methods
- Survey Data Collection Techniques
- Observation Data Collection Methods
- Focus Group Data Collection Strategies
- Interview Data Collection Techniques
- An Introduction to Web Scraping
- Using APIs for Data Collection
- An Overview of Data Wrangling
- Key Data Transformation Techniques
- Normalization and Standardization Methods
- Effective Data Parsing Techniques
04 Exploratory Data Analysis (EDA)
- What is Exploratory Data Analysis?
- Understanding Descriptive Statistics
- Univariate, Bivariate, and Multivariate Analysis Explained
- The Basics of Dimensionality Reduction
- Identifying Trends and Patterns in Data
- Conducting Outlier Analysis
- Exploratory Data Analysis with Python and R: Part 1
- Exploratory Data Analysis with Python and R: Part 2
05 Statistical Analysis
- Introduction to Statistical Analysis
- Basics of Probability Theory
- Sampling Methods and Techniques
- Understanding Hypothesis Testing
- Calculating Confidence Intervals
- Conducting T-tests and ANOVA
- Fundamentals of Regression Analysis
- Performing Chi-Square Tests
- Techniques for Correlation Analysis
- Correlation vs. Causation Explained
- Basics of Time Series Analysis
06 Data Visualization Techniques
- Introduction to Data Visualization
- Data Visualization Techniques: Part 1
- Data Visualization Techniques: Part 2
- Creating Charts in Excel: Part 1
- Creating Charts in Excel: Part 2
- Building Interactive Dashboards in Tableau
- Using Power BI for Data Visualization: Part 1
- Using Power BI for Data Visualization: Part 2
- Creating Visualizations in Python
- D3 for Data Visualization: Part 1
- D3 for Data Visualization: Part 2
- Mastering Storytelling with Data
07 Machine Learning Basics
- Introduction to Machine Learning
- Understanding Supervised vs. Unsupervised Learning
- Overview of Common Machine Learning Algorithms
- Preparing Data for Machine Learning
- Techniques for Feature Selection and Engineering
- Model Training and Evaluation Explained
- Addressing Overfitting and Underfitting
- Cross-Validation Techniques for Model Assessment
- Introduction to Scikit-Learn
- Implementing Linear Regression
- Implementing Logistic Regression
- Understanding Decision Trees
- An Overview of Random Forests
08 Predictive Analytics
- What is Predictive Analytics?
- Understanding Predictive Modeling
- Building Predictive Models: Part 1
- Building Predictive Models: Part 2
- ARIMA and SARIMA Models: Part 1
- ARIMA and SARIMA Models: Part 2
- Ensemble Methods: Bagging and Boosting
09 Big Data Analytics
- Introduction to Big Data
- Understanding the 6 V's of Big Data
- Big Data Technologies: Hadoop and Spark
- Introduction to NoSQL Databases
- Big Data Processing Frameworks Explained
- Understanding HDFS and MapReduce
- Implementing Spark for Big Data Analytics
- Real-Time Data Processing with Kafka
- Applications of Big Data
10 Excel for Data Analytics
- Data Cleaning Techniques in Excel
- Mastering Advanced Pivot Tables
- Using Excel for Statistical Analysis
- Conditional Formatting and Data Bars in Excel
- Automating with Excel Macros
- Using Power Query for Data Transformation
- Using Excel for Financial Modeling
- Excel Tips and Tricks for Data Analysts
11 Python for Data Analytics
- Introduction to Python for Data Analytics
- Understanding Variables in Python
- Working with Lists in Python
- Using Tuples and Strings in Python
- Dictionaries and Sets in Python
- Basic Math Operations in Python for Data Analysis
- Understanding Basic Data Types in Python
- Control Flow in Python: if, else, for, while
12 Pandas Basics for Data Analytics
- What is Pandas in Python?
- Basics of DataFrames
- Different Ways to Create a DataFrame
- Reading and Writing Data in Pandas
- Understanding Pandas DataFrames
13 NumPy Essentials for Data Analytics
- Introduction to NumPy
- NumPy Functions Tutorial: np.ones, np.zeros, np.empty
- NumPy Functions: np.arange(), np.flatten(), np.ravel()
- Slicing Operations in NumPy Arrays
- Performing Mathematical Operations in NumPy
- Trigonometric Operations in NumPy
- Random Operations in NumPy
- String Operations in NumPy
14 Data Manipulation Using Pandas
- Working with Lists and Dictionaries in Pandas
- Pandas DataFrame and Reading CSV Files
- Handling Null Values in Pandas
- Handling Missing Data: fillna, dropna, interpolate
- Handling Missing Data: replace Function
- Group By (Split Apply Combine) in Pandas
- Concatenating DataFrames in Pandas
- Merging DataFrames in Pandas
- Reshaping DataFrames Using Melt
- Stack and Unstack Operations in Pandas
- Using Crosstab in Pandas
- Handling Large Datasets in Pandas
15 Data Cleaning and Preparation with Python
- Exploring and Cleaning Data in Pandas
- Working with Text Data in Pandas
- Dealing with Dates in Pandas
- Joining Data in Pandas
- Creating Frequency Tables in Pandas
16 R for Data Analytics
- Understanding Variables in R
- Working with Vectors and Lists in R
- Creating and Using Matrices in R
- Understanding Arrays in R
- Working with Data Frames in R
- Understanding Factors in R
- Basic Math Operations in R
- Understanding Data Types in R
- Using Control Structures in R
- Defining and Using Functions in R
17 Data Manipulation with dplyr in R
- Introduction to dplyr in R
- Selecting Columns with select() in dplyr
- Filtering Rows with filter() in dplyr
- Arranging Rows with arrange() in dplyr
- Mutating Columns with mutate() in dplyr
- Summarizing Data with summarize() in dplyr
- Group By (Split Apply Combine) in dplyr
- Joining Data Frames with join() in dplyr
18 Data Cleaning and Preparation with R
- Exploring Data in R: Part 1
- Exploring Data in R: Part 2
- Cleaning Data in R: Part 1
- Cleaning Data in R: Part 2
- Working with Text Data in R: Part 1
- Working with Text Data in R: Part 2
- Preparing Numeric Data in R
- Dealing with Dates in R
- Handling Missing Data in R
- Handling Duplicates in R
- Converting Data Types in R
19 Machine Learning with R for Data Analytics
- Introduction to Machine Learning with R
- Building Linear Models in R
- Creating Decision Trees in R
- Using Random Forests in R
- Implementing Support Vector Machines in R
20 Working with Large Datasets in R
- Introduction to Data.table in R: Part 1
- Introduction to Data.table in R: Part 2
- Handling Large Datasets in R
- Efficient Data Manipulation with Data.table
- Connecting to Databases from R
- Reading and Writing Data in R
21 SQL for Data Analytics
- Introduction to SQL for Data Analytics
- Setting Up Databases, Tables, and Values
- Writing SELECT Statements in SQL
- Using the WHERE Clause in SQL
- Sorting Data with ORDER BY in SQL
- Using JOINs in SQL
- Using UNION and UNION ALL in SQL
- Applying Aggregate Functions in SQL
- Grouping Data with GROUP BY in SQL
- Writing Subqueries in SQL
- Understanding SQL Window Functions
- SQL Query Optimization Techniques
22 Data Manipulation and Transformation with SQL
- Approaching Complex SQL Queries
- Inserting Data with INSERT INTO
- Updating Data with UPDATE
- Deleting Data with DELETE
- Working with Dates and Times in SQL
23 Data Cleaning in SQL
- Data Cleaning in SQL: Part 1
- Data Cleaning in SQL: Part 2
- Data Cleaning in SQL: Part 3
- Data Cleaning in SQL: Part 4
- Data Cleaning in SQL: Part 5
24 Data Analytics Tools
- The Nine Essential Tools for Data Analytics
- Overview of Google Analytics
- Introduction to Tableau
- Introduction to Power BI
Real-world projects you'll work on
Practice the exact kind of work Data Analytics professionals do on the job.
Data-Driven Decision Making: Optimizing Customer Experience at a Global E-Commerce Giant
Uncovering Market Insights: Leveraging Data Analytics for Strategic Expansion at a Tech Startup
Enhancing Operational Efficiency: Implementing Data Analytics Solutions at a Leading Financial Services Firm
Predictive Maintenance and Risk Mitigation: Harnessing Data Analytics for Asset Management in a Multinational Manufacturing Company
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Data Analytics course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- College students looking to build a career in data science.
- Working professionals wanting to transition into tech roles.
- Business analysts who need to upgrade their Python and SQL skills.
- Anyone seeking a free data analyst course to learn data modeling.
Prerequisites
- No prior coding or statistics experience is required to start.
- Basic familiarity with high school mathematics is helpful.
- A computer with an internet connection to run basic tools.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Data Analytics course: free, structured and job-focused
You'll start by writing SQL queries to extract data and building Pandas dataframes in Python to clean messy datasets. This free data analytics course moves from basic concepts to advanced statistical analysis and machine learning basics. Across 197 video lessons, you will master essential tools like Excel, Python, and R. You'll cover specific techniques including Exploratory Data Analysis (EDA) and predictive analytics. The curriculum even trains you on working with large datasets in R and using NumPy essentials for data analytics.
Theory only goes so far, so you will apply these skills to four hands-on projects. You'll tackle real scenarios like optimizing customer experience at a global e-commerce giant and uncovering market insights for strategic expansion at a tech startup. Other projects involve enhancing operational efficiency for a financial firm and planning predictive maintenance for a manufacturing company. These practical builds give you a portfolio to show future employers.
LearnTube makes mastering data analytics classes online simple. We pair top YouTube video lessons with an interactive AI tutor that tests your knowledge. After every video, you'll take a quick quiz to ensure you actually understand the material. It's completely free to learn. If you want to verify your skills for recruiters from our 900+ hiring partners, you can purchase an optional certificate at the end.
Jobs you can target after learning Data Analytics
Data Analytics is a gateway skill for some of the fastest-growing roles in tech.
Cleans, analyzes, and visualizes data to help businesses make informed decisions.
Builds predictive models and applies machine learning algorithms to complex datasets.
Creates dashboards and reports to track key performance metrics for company leadership.
Typical entry-level salary ranges in India as of July 2026. Actual compensation varies by city, company and experience.
Earn a Data Analytics certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Data Analytics course.
What tools or software do I need installed?
You will need a standard web browser to watch the lessons. For hands-on practice, you should install Excel, Python, and SQL. Free versions or online compilers work perfectly for all these tools.
Do I need prior coding experience to take this data analytics course?
No previous programming knowledge is necessary. The curriculum starts with the very basics of data analytics before moving into Python and SQL. Beginners can comfortably follow along and build their skills step by step.
What job roles can I get after finishing these data analytics classes?
You can pursue roles like data analyst, business intelligence analyst, or junior data scientist. Entry-level data analysts in India typically earn between ₹4-6 LPA. Your exact salary will depend on your location, skills, and the hiring company.
Can I study on my mobile phone at my own pace?
Yes, LearnTube is fully mobile-friendly. You can watch the 197 video lessons and take the AI quizzes directly on your phone. The course is entirely self-paced, so you can study whenever you have free time.
Are there any data analytics course fees?
The learning content is entirely free. You can watch all the video lessons and complete the projects without paying anything. You only pay if you choose to buy the optional completion certificate at the end.
How long is the data analyst course duration?
The complete curriculum takes about 34 hours to finish. Since the material is self-paced, you can spread this time over a few weeks or complete it faster if you study for a few hours every day.
Does this free data analyst course online include projects?
Yes, you will complete four practical projects. These include optimizing e-commerce customer experiences and planning predictive maintenance for manufacturing. These hands-on builds help you apply your new coding skills to real business problems.
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197 lessons, an AI tutor that never gets tired, and a certificate at the finish line. All you need is an hour a day.
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