Learn Data Analytics Free — Tutorials & Practice for Students

✓ 100% Free ✓ No Login Needed ✓ NCERT / CBSE Aligned ✓ Download as PDF

TL;DR: Free Data Analytics tutorials, examples and coding practice for Indian students. Beginner to advanced, with an in-browser editor and AI feedback — no…

🤖 Stuck on any question? Ask Syllab's free AI Tutor for a step-by-step explanation — instant, unlimited, no login.

Free Data Analytics course: 49 lessons, from the basics through to the advanced topics interviewers actually ask about. Every lesson is free, needs no sign-up, and runs in your browser with an editor and instant AI feedback.

Data Analytics Course Syllabus — 49 Lessons

Work through them in order, or jump to the topic you need. 49 lessons include worked explanations and runnable examples.

Data Fundamentals

• What is Data Analytics? — Data Analytics is the science of examining raw data to draw conclusions, discover patterns, and extract actionable insights.
• Types of Data — Structured, Unstructured, Quantitative — Data comes in different types, and understanding the type determines what analysis you can do.

Statistics

• Mean, Median, Mode — Measures of Central Tendency — Measures of central tendency describe where the "centre" of a dataset is.
• Statistical Analysis — Mean, Median, Mode & Beyond — Statistics is the mathematical backbone of data analytics.
• Correlation & Regression — Finding Relationships in Data — Correlation measures the strength and direction of a relationship between two variables.
• Pearson Correlation — Measuring Relationship Strength — Pearson correlation (r) measures linear relationship between two variables.
• IQR Method for Outlier Detection — Interquartile Range (IQR) = Q3 - Q1 (middle 50% of data).
• Descriptive Statistics — Describe data with measures: mean (average), median (middle), mode (most frequent).
• Introduction to Hypothesis Testing — Hypothesis testing determines if sample evidence supports a claim about a population.

Data Processing

• Data Cleaning — Making Messy Data Usable — Data cleaning (also called data wrangling or data munging) is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data.
• Data Cleaning — Making Messy Data Usable — Data cleaning (also called data wrangling or data munging) is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data.

Data Visualisation

• Charts and Graphs — Visualising Data — Data visualisation turns numbers into pictures.

Working with Data Files

• CSV Files — The Most Common Data Format — CSV (Comma-Separated Values) is the most universal data format — virtually every data tool (Excel, Python, R, SQL databases, Power BI) can read and write CSV.

Data Analysis

• Finding Patterns and Trends in Data — Pattern recognition is the heart of data analytics.
• Outliers — Finding the Unusual in Data — An outlier is a data point that is significantly different from other observations.

Python for Data

• Python Pandas — The Most Powerful Data Tool — Pandas is Python's most important data analytics library.
• NumPy Arrays — Fast Numerical Computing — NumPy (Numerical Python) is the foundation of the entire Python data science stack.
• Pandas DataFrames — Excel in Python — Pandas is the most important library for data analysis in Python.

Data Communication

• Data Storytelling — Communicating Insights Clearly — Data storytelling is the ability to communicate data insights in a clear, compelling narrative that drives action.

Data Tools

• SQL for Data Analytics — Querying Large Datasets — SQL (Structured Query Language) is essential for data analytics when data lives in databases (which is most of the time in business).
• SQL for Data Analytics — Querying Large Datasets — SQL (Structured Query Language) is essential for data analytics when data lives in databases (which is most of the time in business).

Projects

• Mini Project — Student Performance Dashboard — Building a complete data analytics project consolidates everything learned.
• Project: Titanic Dataset Exploratory Data Analysis — The Titanic dataset is a classic in data science.

Data Visualization

• Data Visualization — Turning Numbers into Insights — Data Visualization is the graphical representation of data and information.

Applied Analytics

• Business Analytics — Data-Driven Decision Making — Business Analytics is applying data analysis techniques to solve real business problems — improving profits, reducing costs, understanding customers, and making better strategic decisions.
• Data Storytelling — Communicating Insights Effectively — Data storytelling is the ability to communicate data insights in a clear, compelling, and actionable way to non-technical audiences.

Pandas Advanced

• Pandas MultiIndex — Hierarchical Data Structures — MultiIndex (multi-level index) is used when your data has more than one level of categories.
• Pivot Tables — Reshaping Data for Analysis — A pivot table reorganises data by taking values from columns and placing them into new rows/columns.
• Melt and Stack — Reshaping Long vs Wide Data — Data comes in two shapes: Wide (many columns, few rows) and Long (few columns, many rows).
• Pandas Merge Types — Inner, Left, Right, Outer Joins — Merging combines two DataFrames based on a common column (like SQL JOIN).

NumPy

• NumPy Broadcasting — Broadcasting allows NumPy operations on arrays of different shapes.
• NumPy Broadcasting — Broadcasting allows NumPy operations on arrays of different shapes.
• NumPy Linear Algebra — Linear algebra in NumPy via np.linalg module: matrix operations for data analysis.
• np.where() for Conditional Operations — np.where() applies conditional logic to arrays: returns different values based on condition.

Data Manipulation

• Regular Expressions in Pandas — Regular expressions (regex) are powerful patterns for matching text.
• MultiIndex DataFrames — MultiIndex (hierarchical indexing) allows multiple levels of row or column indices.
• Pivot Tables — Pivot tables reshape data by specifying index, columns, and values.
• Melt, Stack, and Unstack Operations — Melt converts wide data to long format: one column becomes index, rest become (variable, value) pairs.
• Merge Types (Inner, Outer, Left, Right) — Inner merge keeps only matching rows from both DataFrames.
• Apply with Custom Functions — Apply allows applying custom functions to DataFrame rows, columns, or elements.
• Regular Expressions in Pandas — Regular expressions (regex) are powerful patterns for matching text.

Data Cleaning

• Detecting and Handling Duplicates — Duplicates are exact or near-exact copies of rows.
• Type Conversion and Casting — Convert data types with pd.to_numeric(), pd.to_datetime(), astype().
• Detecting and Handling Duplicates — Duplicates are exact or near-exact copies of rows.

Time Series

• Time Series — Date Parsing and Time-Based Indexing — Time series data has dates as the index. Examples: stock prices over days, temperature over months, website traffic over hours.

Visualization

• Matplotlib Subplots — Subplots arrange multiple plots in a grid (rows × columns).
• Seaborn Heatmaps — Heatmaps visualize 2D data using color intensity.
• Seaborn Pairplots — Pairplots show pairwise relationships between variables in a dataset.
• Box Plots for Distributions — Box plots show distribution: box contains Q1-Q3 (IQR), line is median, whiskers extend to min/max.

Who this Data Analytics course is for

School and college students in India starting from zero, and anyone revising Data Analytics for placements, board practicals or a project. There is no prerequisite beyond being able to type — the first lessons assume no programming background at all.

How to study this course

1. Read the lesson, then run the example in the built-in editor before moving on — reading code is not the same as writing it.
2. Try the practice task at the end of each lesson. Getting it wrong and fixing it is where the learning happens.
3. When you are stuck, ask the free AI Tutor to explain that specific line rather than skipping ahead.

🤖 Stuck on any of these? Ask Syllab's free AI Tutor to explain step by step →