Statistics & Probability for Data Science

About Course

This module builds the critical statistical thinking required for Data Science, AI, and Machine Learning.
You will learn descriptive statistics, probability basics, distributions, hypothesis testing, and statistical inference — the foundation for understanding machine learning algorithms deeply.

Course Content

Introduction to Statistics & Probability and its Role in Data Science

Why Statistics & Probability Matter in Data Science
The Language of Data: Basic Statistical Concepts
Probability Fundamentals for Predictive Power
Statistics vs Probability: Friends, Not Enemies
How Statistics Powers Machine Learning + Tools Overview

Linear Algebra Basics (Vectors, Matrices, Operations)

Probability Fundamentals (Mean, Median, Mode, Variance, SD)

Combinatorics & Probability Distributions

Hypothesis Testing (Z-test, T-test, p-value concepts)

Correlation and Regression Basics

Introduction to Gradient Descent
Key Takeaways from the 5 Lessons: Comprehensive understanding of Gradient Descent, from theory to implementation. Real-world applications of GD in optimizing machine learning models. Hands-on experience through Python coding to make concepts actionable.

Mini Project: Predict housing prices using correlation analysis

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About Course

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Course Content

Introduction to Statistics & Probability and its Role in Data Science

Why Statistics & Probability Matter in Data Science

The Language of Data: Basic Statistical Concepts

Probability Fundamentals for Predictive Power

Statistics vs Probability: Friends, Not Enemies

How Statistics Powers Machine Learning + Tools Overview

Linear Algebra Basics (Vectors, Matrices, Operations)

The Language of Linear Algebra: Vectors and Scalars

Vectors: The Building Blocks of Data

Matrices: Organizing Data

Matrix-Vector Multiplication and its Applications

Determinants, Inverse Matrices, and Eigenvalues

Probability Fundamentals (Mean, Median, Mode, Variance, SD)

Introduction to Probability & Descriptive Statistics

Measures of Central Tendency: Mean, Median, Mode

Variance: Understanding Data Spread

Standard Deviation: Bringing it All Together

Real-World Applications & How to Interpret These Metrics

Combinatorics & Probability Distributions

Introduction to Combinatorics and Counting Principles

Understanding Probability and Its Role in Combinatorics

Introduction to Probability Distributions

Deep Dive into the Binomial Distribution

The Normal Distribution & Central Limit Theorem

Hypothesis Testing (Z-test, T-test, p-value concepts)

Introduction to Hypothesis Testing

The Z-Test – When and How to Use It

The T-Test – A Deeper Dive for Smaller Samples

Understanding p-Values and Significance Levels

Applying Hypothesis Testing with Real Data (Hands-on)

Bonus Mini Project

Correlation and Regression Basics

Understanding Correlation: The Relationship Between Variables

Types of Correlation: Pearson, Spearman, and Kendall

Calculating Correlation in Python

Introduction to Regression Analysis

Simple Linear Regression: From Data to Prediction

Introduction to Gradient Descent Key Takeaways from the 5 Lessons: Comprehensive understanding of Gradient Descent, from theory to implementation. Real-world applications of GD in optimizing machine learning models. Hands-on experience through Python coding to make concepts actionable.

What is Gradient Descent?

Mathematical Intuition of Gradient Descent

Types of Gradient Descent

Challenges in Gradient Descent

Practical Implementation of Gradient Descent in Python

Mini Project: Predict housing prices using correlation analysis

Understanding the Dataset: Housing Features and Prices

Exploratory Data Analysis (EDA): Discover Patterns

Correlation Analysis: Finding the Key Drivers

Simple Linear Regression: Making Predictions

Project Wrap-Up: Interpretation, Limitations, and Reporting

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Introduction to Gradient Descent
Key Takeaways from the 5 Lessons: Comprehensive understanding of Gradient Descent, from theory to implementation. Real-world applications of GD in optimizing machine learning models. Hands-on experience through Python coding to make concepts actionable.