Learn Data Science Online: Free Course with AI Tutor & Certificate
You'll start by analyzing real datasets using Python, SQL, and R. This free data science course spans 24 modules and about 34 hours of video lessons. You will master data wrangling, statistical analysis, and machine learning basics, preparing you to extract valuable patterns from raw information.
What you'll learn in this Data Science course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Perform data analysis and manipulation using NumPy and Pandas.
Utilize Python for data analytics, including setting up environments and understanding control structures.
Apply advanced NumPy techniques for scientific computing and handling missing data.
Visualize and analyze data using Pandas, Matplotlib, Seaborn, and Plotly.
Complete real-world projects involving exploratory data analysis with Pandas.
Data Science 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 Science professionals do on the job.
Data Analytics for Global E-commerce Giant: Uncovering Market Trends and Customer Behavior
Data-Driven Decision Making at a Tech Startup: Optimizing Product Performance and User Engagement
Revolutionizing Healthcare Analytics at a Leading Pharmaceutical Company: Leveraging Data for Patient Insights
Transforming Financial Services with Data Science: Predictive Analytics and Risk Management for a Fintech Innovator
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Data Science course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- College students in India looking for a data science course after 12th to start building technical skills early.
- Working professionals who want to transition into tech roles using flexible data science online classes.
- Career switchers aiming to learn data science without paying high upfront data science course fees.
- Anyone interested in mastering Python, SQL, and R for analyzing large datasets.
Prerequisites
- No prior experience is required to begin this course.
- Basic familiarity with high school mathematics and statistics is helpful.
- Access to a computer with an internet connection to run Python and R environments.
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 Science course: free, structured and job-focused
You'll begin by visualizing and analyzing raw data using Pandas, Matplotlib, and Seaborn. This data science course online covers everything from the Basics of Data Analytics to Machine Learning Basics. Across 24 modules and 197 video lessons, you will learn to manipulate information using Python, R, and SQL. The curriculum moves through core topics like Data Collection and Data Wrangling, Exploratory Data Analysis (EDA), and Predictive Analytics. You will even explore Big Data Analytics and Excel for Data Analytics to ensure you have a versatile skill set.
LearnTube makes studying efficient and interactive. We curate the best YouTube video lessons and pair them with an AI tutor that tests your knowledge. After every video, you take a quick quiz to check your understanding. It's entirely self-paced, so you can learn completely for free whenever you have time. If you want to prove your new skills to employers, you can upgrade to an optional paid certificate.
To build real experience, you'll complete four hands-on projects. You will analyze market trends for a global e-commerce giant, optimize product performance for a tech startup, uncover patient insights for a pharmaceutical company, and handle predictive analytics for a fintech innovator. By the end of the 34 hours of content, you will know how to perform data manipulation using NumPy and Pandas, setting you up for success in the data field.
Jobs you can target after learning Data Science
Data Science is a gateway skill for some of the fastest-growing roles in tech.
Cleans and interprets raw data to help businesses make informed decisions.
Builds basic machine learning models and performs predictive analytics.
Creates dashboards and reports to visualize company performance metrics.
Typical entry-level salary ranges in India as of August 2026. Actual compensation varies by city, company and experience.
Earn a Data Science 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 Science course.
What is the data science course syllabus?
The curriculum includes 24 modules covering Python, SQL, R programming, and machine learning. You will study data wrangling, statistical analysis, and data visualization. The content features 197 video lessons and four hands-on projects to build your practical skills.
What are the data science course fees?
Learning on LearnTube is completely free. You can watch all 197 video lessons and take the AI quizzes without paying anything. We only charge a small fee if you choose to purchase the optional data science certification.
What is the data science course duration?
It takes about 34 hours to complete all the video lessons and AI quizzes. Because the platform is entirely self-paced, you can finish the material in a few weeks or stretch it out over several months.
Can I take this data science course after 12th?
Yes. You do not need a college degree to start learning. This beginner-friendly program begins with an Introduction to Data Analytics, making it perfect for recent high school graduates wanting to build highly sought-after technical skills.
What jobs can I get after a data science training?
You can pursue entry-level roles like data analyst, junior data scientist, or business intelligence analyst. These positions typically involve cleaning datasets, building visualizations, and running predictive models for companies across various industries.
Does this platform work on mobile devices?
Yes, you can access all videos and AI quizzes directly from your smartphone. This makes it easy to study on the go and complete your lessons whenever you have free time during your day.
What projects will I build in these data science classes?
You will complete four practical assignments. These include analyzing market trends for an e-commerce giant, optimizing user engagement for a tech startup, finding healthcare insights, and managing risk for a financial technology company.
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