Data Science — Free Notes & Tutorial
Free data science course covering statistics, pandas, numpy, visualization and ML pipelines. Learn data science at SikshaSarovar.
This Data Science course is part of Siksha Sarovar and is 100% free for students in India — no sign-up required to read. It contains 37 structured lessons with examples, and pairs with our free online compiler and AI tutor.
What you will learn
- Statistics
- Pandas
- Numpy
- Data visualization
- Machine learning
Course content (37 lessons)
- Unit 1: Foundation of Data Science — Unit 1: Foundation of Data Science This unit establishes the groundwork for understanding Data Science as a discipline. We will explore the fundamental concepts, the complete…
- What is Data Science? — What is Data Science? Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract meaningful knowledge and insights from…
- Data Science Lifecycle — Data Science Lifecycle The Data Science Lifecycle is the structured, iterative process that a data science project follows from inception to deployment. It is not a strictly…
- Roles: Analyst vs Scientist vs Engineer — Roles in the Data Ecosystem The data industry has several distinct professional roles. While they often overlap, each has a unique focus, skill set, and contribution to the data…
- Applications of Data Science — Applications of Data Science Data Science has transformed how industries operate, make decisions, and serve customers. From predicting disease outbreaks to recommending your next…
- Introduction to Big Data — Introduction to Big Data The term "Big Data" refers to datasets that are so large, fast-moving, or complex that they cannot be processed or analyzed using traditional data…
- Types of Data: Structured, Unstructured & Semi-Structured — Types of Data Understanding the different types of data is fundamental to Data Science, because the type of data determines which tools, storage systems, and analytical techniques…
- Unit 2: Mathematics for Data Science — Unit 2: Mathematics for Data Science Mathematics is the backbone of Data Science . Every algorithm, every model, and every insight is fundamentally grounded in mathematical…
- Basic Mathematics for Data Science — Basic Mathematics for Data Science Before diving into Linear Algebra or Probability, it is essential to be comfortable with fundamental mathematical concepts that appear…
- Linear Algebra: Vectors & Matrices — Linear Algebra: Vectors & Matrices Linear Algebra is the branch of mathematics dealing with vectors, matrices, and linear transformations. It is arguably the most important…
- Matrix Operations & Eigenvalues — Matrix Operations Matrix operations are the computational backbone of Machine Learning. Understanding how matrices are added, multiplied, and decomposed is essential for grasping…
- Probability & Bayes Theorem — Probability for Data Science Probability is the mathematical framework for quantifying uncertainty . In data science, almost everything involves uncertainty — from predicting…
- Statistics: Mean, Median, Mode, Variance & SD — Descriptive Statistics Statistics is the science of collecting, analyzing, interpreting, and presenting data. Descriptive Statistics summarizes and describes the main features of…
- Hypothesis Testing & Confidence Intervals — Hypothesis Testing Hypothesis Testing is a structured, statistical method for making decisions about a population based on sample data. It helps answer questions like: "Is the…
- Unit 3: Python Programming — Unit 3: Python Programming Python is the most popular programming language for Data Science. Its simple syntax, vast ecosystem of libraries, and strong community support make it…
- Introduction to Python — Introduction to Python What is Python? Python is a high-level, interpreted, general-purpose programming language created by Guido van Rossum in 1991 . It emphasizes code…
- Variables & Data Types — Variables & Data Types in Python Variables Definition: A variable is a named container that stores a value in memory.In Python, you do not need to declare the type — it is…
- Operators in Python — Operators in Python Definition: An operator is a symbol that performs an operation on one or more operands(values / variables).Python supports a rich set of operators across…
- Loops & Conditional Statements — Conditional Statements Conditional statements allow Python to make decisions based on conditions.They control the flow of execution by running different blocks of code depending…
- Functions in Python — Functions in Python Definition: A function is a reusable block of organized code that performs a specific task.Functions help break large programs into smaller, manageable, and…
- Object-Oriented Programming (OOP) — Object - Oriented Programming(OOP) in Python Definition: Object - Oriented Programming is a programming paradigm that organizes code into objects — bundles of data(attributes)…
- File Handling in Python — File Handling in Python Definition: File handling refers to the ability to read from and write to files on the file system.In Data Science, you constantly work with files —…
- Exception Handling in Python — Exception Handling in Python Definition: Exception Handling is a mechanism in Python that allows you to gracefully handle runtime errors instead of letting the program…
- Unit 4: Python Libraries for Data Science — Unit 4: Python Libraries for Data Science Python's dominance in Data Science is largely due to its powerful ecosystem of open-source libraries . These libraries provide pre-built,…
- NumPy: Numerical Computing — NumPy (Numerical Python) Definition: NumPy is the foundational library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices ,…
- Pandas: Data Manipulation — Pandas: Data Manipulation & Analysis Definition: Pandas is the most important library for data manipulation and analysis in Python.It provides two primary data structures —…
- Matplotlib: Data Visualization — Matplotlib: Data Visualization Definition: Matplotlib is the most widely used library for creating static, animated, and interactive visualizations in Python. It provides…
- Seaborn: Statistical Visualization — Seaborn: Statistical Data Visualization Definition: Seaborn is a Python visualization library built on top of Matplotlib that provides a high-level interface for creating…
- Scikit-learn: Machine Learning — Scikit-learn: Machine Learning in Python Definition: Scikit-learn (sklearn) is the most popular machine learning library in Python. It provides simple and efficient tools for data…
- SciPy: Scientific Computing — SciPy: Scientific & Statistical Computing Definition: SciPy(Scientific Python) is an open - source library that builds on NumPy to provide additional functionality for scientific…
- Unit 5: Data Manipulation & Analysis — Unit 5: Data Manipulation & Analysis Data Manipulation & Analysis is the heart of the data science workflow . Before any model can be trained or any insight communicated, the raw…
- Data Cleaning — Data Cleaning Data Cleaning (also called Data Cleansing or Data Scrubbing ) is the process of identifying and correcting (or removing) errors, inconsistencies, and inaccuracies in…
- Handling Missing Values — Handling Missing Values Missing values are one of the most pervasive data quality issues in real-world datasets. How you handle them can significantly impact the accuracy and…
- Outlier Detection — Outlier Detection Outliers are data points that deviate significantly from the majority of observations. They can arise from data entry errors, measurement faults, or genuine rare…
- Data Transformation — Data Transformation Data Transformation is the process of converting data from one format, structure, or value range into another. It is a critical preprocessing step that ensures…
- Feature Engineering — Feature Engineering Feature Engineering is the process of using domain knowledge and creativity to create new features (variables) from existing raw data, or to select and…
- Exploratory Data Analysis (EDA) — Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods. It is the…
Unit 1: Foundation of Data Science
Unit 1: Foundation of Data Science
This unit establishes the groundwork for understanding Data Science as a discipline. We will explore the fundamental concepts, the complete lifecycle of a data science project, the various professional roles within the industry, real-world applications across sectors, and the foundational concepts of Big Data and data types.
Key Topics Covered:
- What is Data Science? — Understanding the interdisciplinary field that combines statistics, computer science, and domain expertise.
- Data Science Lifecycle — The step-by-step iterative process from problem definition to actionable insights.
- Roles in Data Science — Differentiating between Data Analyst, Data Scientist, and Data Engineer.
- Applications of Data Science — Real-world use cases across healthcare, finance, e-commerce, and more.
- Introduction to Big Data — The 5 Vs and why traditional tools fail.
- Types of Data — Structured, Unstructured, and Semi-Structured data categories.
What is Data Science?
What is Data Science?
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract meaningful knowledge and insights from noisy, structured, and unstructured data. It sits at the intersection of three core domains:
- Statistics & Mathematics: For modeling, probability, and quantitative analysis.
- Computer Science & Programming: For building algorithms, automating tasks, and handling large datasets.
- Domain Knowledge: For understanding the real-world context in which data exists.
Formal Definition
Data Science is the systematic study of data through the application of quantitative and analytical approaches to derive actionable insights. It encompasses a broad range of techniques from descriptive statistics to advanced machine learning. Unlike traditional business analytics, Data Science seeks not only to understand what happened in the past, but also to predict what will happen in the future and prescribe optimal actions.
Why Data Science Matters
The explosion of digital data in the 21st century has created an unprecedented need for skilled professionals who can turn raw data into strategic value. According to industry research:
- Every day, approximately 2.5 quintillion bytes of data are created.
- Over 90% of the world's data has been generated in just the last two years.
- Companies that leverage data-driven decision-making are, on average, 5% more productive and 6% more profitable than their competitors.
Data Science provides the tools and methodologies to harness this data deluge and convert it into a competitive advantage.
Core Pillars of Data Science
| Pillar | Description | Example |
|---|---|---|
| Statistics | Foundation for understanding data distributions, sampling, and hypothesis testing | A/B Testing on a website |
| Machine Learning | Algorithms that learn from data to make predictions or decisions | Spam email filter |
| Data Engineering | Infrastructure to collect, store, and process large datasets | Building a data pipeline |
| Visualization | Presenting insights in a clear, actionable format | Interactive dashboards |
| Domain Expertise | Contextual understanding of the business or field | Medical diagnosis rules |
Data Science vs Related Fields
It is important to distinguish Data Science from closely related fields:
| Feature | Data Science | Artificial Intelligence | Machine Learning | Statistics |
|---|---|---|---|---|
| Goal | Extract insights from data | Simulate human intelligence | Learn patterns from data | Analyze and interpret data |
| Scope | Broad — encompasses ML, Stats, Engineering | Broad — includes ML, NLP, Robotics | Subset of AI | Foundation of Data Science |
| Output | Insights, Predictions, Reports | Intelligent Systems | Predictive Models | Estimates, Hypothesis Tests |
| Example | Customer churn analysis | Self-driving car | Email spam classifier | Clinical trial analysis |
Key Terminology
- Dataset: A structured collection of data, often represented as a table with rows (records) and columns (features).
- Feature (Variable): An individual measurable property of the data (e.g., Age, Income, Temperature).
- Label (Target): The outcome variable that a model tries to predict (e.g., "Yes/No" for fraud).
- Model: A mathematical representation of a real-world process, trained on data to make predictions.
- Algorithm: A step-by-step procedure for solving a problem or performing a computation.
The Data Science Venn Diagram
Data Science is famously represented as the intersection of three circles:
- Hacking Skills (Computer Science): The ability to write code, manipulate data, and use tools.
- Math & Statistics Knowledge: Understanding the theory behind the models and analysis.
- Substantive Expertise (Domain Knowledge): Knowing which questions to ask and how to interpret results in context.
The "sweet spot" where all three overlap is where true Data Science happens. Without domain knowledge, you may build accurate but meaningless models. Without statistics, your conclusions may be flawed. Without programming, you cannot implement your ideas at scale.
Summary
- Data Science is an interdisciplinary field combining math, computing, and domain knowledge.
- It aims to extract actionable insights from data.
- It is distinct from, but related to, AI, ML, and traditional statistics.
- The field is driven by the massive growth of data in the modern world.
Frequently asked questions
Is the Data Science course really free?
Yes. The entire Data Science course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.
Do I get a certificate for Data Science?
Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.
Can I run code while learning?
Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.