Learn Python for Data Analysis Online: Free Course with AI Tutor & Certificate
You will learn to clean, analyze, and visualize complex datasets using Pandas and NumPy. This free python for data analysis course spans 34 hours across 24 modules and 197 video lessons. You'll build five real projects, like a Personal Finance Tracker, preparing you to tackle real-world data challenges.
What you'll learn in this Python for Data Analysis 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 importance in business strategy.
Perform data collection and data wrangling using various techniques and tools.
Conduct exploratory data analysis to identify trends, patterns, and outliers in data.
Apply statistical analysis techniques to draw meaningful insights from data.
Use data visualization techniques to effectively communicate data findings.
Python for Data Analysis 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 Python for Data Analysis professionals do on the job.
Social Network Data Miner: Build a data mining application using list comprehensions, dictionaries, and sets to parse and summarize connections, posts, and user activity.
Library Management System: Design a system using object-oriented programming concepts in Python where users can add books, borrow books, and return books, with details structured in classes and objects.
Personal Finance Tracker with Python
Automated Resume Screening System
Smart Agriculture Dashboard with IoT Data Integration
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Python for Data Analysis course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- College students in India looking to enter the data science field.
- Working professionals wanting to transition into a data analyst role.
- Business analysts seeking to upgrade from Excel to Python.
- Software developers interested in adding data analytics to their skillset.
Prerequisites
- No prior data analytics experience is required to begin this course.
- Basic familiarity with high school mathematics and statistics is helpful.
- A computer with internet access to write and execute Python code.
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 Python for Data Analysis course: free, structured and job-focused
You will start by writing scripts to process messy datasets and find hidden patterns. This data analysis using python course walks you through the complete lifecycle of data handling. You'll begin with the Basics of Data Analytics and move quickly into Data Collection and Data Wrangling. From there, you will master Exploratory Data Analysis (EDA) and Data Visualization Techniques to make sense of numbers. The curriculum also covers essential libraries, featuring modules like Pandas Basics for Data Analytics and NumPy Essentials for Data Analytics.
Theory only gets you so far, so you will apply these concepts immediately. You'll complete five hands-on projects, including a Social Network Data Miner and an Automated Resume Screening System. The curriculum goes beyond basic Python, touching on Big Data Analytics, SQL for Data Analytics, and even Machine Learning Basics. We also cover Data Cleaning and Preparation with Python to ensure you know how to handle real, imperfect data.
LearnTube makes studying efficient and interactive. Our platform takes the best curated YouTube video lessons and pairs them with an AI tutor that generates a quiz after every single lesson. You can study the entire 34-hour syllabus completely for free. If you want to verify your new skills, an optional paid certificate is available to add to your resume.
Jobs you can target after learning Python for Data Analysis
Python for Data Analysis is a gateway skill for some of the fastest-growing roles in tech.
Cleans, interprets, and visualizes data to help companies make informed decisions.
Writes backend code and scripts to automate data collection and processing tasks.
Transforms raw data into interactive dashboards for business leaders.
Typical entry-level salary ranges in India as of August 2026. Actual compensation varies by city, company and experience.
Earn a Python for Data Analysis 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 Python for Data Analysis course.
Is using python for data analysis still in demand?
Yes. Companies rely heavily on data to make decisions, and Python is the most popular language for this work. Learning these skills makes you highly employable across finance, tech, and healthcare sectors in India.
Is this python data analytics course really free?
Yes, the learning content is completely free. You get full access to all 197 video lessons and AI-generated quizzes at no cost. You only pay if you choose to upgrade for the optional verified certificate.
Will I get a certificate after completing the syllabus?
You can earn an optional paid certificate once you finish the modules. This verifiable document highlights your new data manipulation skills and can be attached to your LinkedIn profile or resume to show employers.
How does this differ from watching regular YouTube tutorials?
Regular videos lack structure and testing. LearnTube organizes the best video lessons into a logical 24-module path and adds an AI tutor that tests your knowledge with a quiz after every single video.
Do I need to know programming before starting?
No prior coding experience is strictly necessary. The course starts with Understanding Data Analytics and builds up gradually. However, having a basic understanding of computer logic will help you grasp the Python concepts faster.
How long does this data analysis python course take?
The entire curriculum contains about 34 hours of video content. Since it is entirely self-paced, you can complete the 24 modules over a few weeks or take your time spreading it across several months.
Are there any hands-on projects included?
Yes, you will build five practical projects. These include a Social Network Data Miner and a Smart Agriculture Dashboard with IoT Data Integration. Building these applications helps solidify the theoretical concepts you learn.
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