Course Title: Master Data Science

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 Duration: ~4.5 Months (16–20 Weeks)

Total Hours: ~90 hours

Format: Weekly 4–6 hours, including lectures, hands-on labs, assignments, and project work

Module 1: Foundations of Python Programming (15 Hours)

Objective: Build strong Python basics tailored for data tasks.

Topics:

  • Introduction to Python & IDEs (Pycharm, Jupyter, VSCode, Colab)

  • Variables, Data Types, Operators

  • Control Structures: if, for, while

  • Functions & Scope

  • Data Structures: Lists, Tuples, Dictionaries, Sets

  • String Manipulation

  • File Handling

  • Error Handling & Debugging

  • Working with datetime, os, sys

Lab: Mini-project – Expense Tracker using core Python

Module 2: SQL for Data Science (10 Hours)

Objective: Learn how to query, filter, and join datasets using SQL.

Topics:

  • Introduction to Databases & Relational Models

  • Basic SQL Queries: SELECT, WHERE, ORDER BY

  • Filtering & Pattern Matching (LIKE, IN, BETWEEN)

  • Aggregate Functions: COUNT, SUM, AVG, etc.

  • GROUP BY & HAVING

  • JOINs: INNER, LEFT, RIGHT, FULL

  • Subqueries, CTEs, and Window Functions

  • Creating and Populating Tables

Lab: SQL Project – Analyzing a mock retail database

Module 3: Statistics & Probability for Data Science (15 Hours)

Objective: Grasp essential statistical methods for data analysis and ML.

Topics:

  • Descriptive Statistics: Mean, Median, Mode, Variance, Std Dev

  • Probability Theory Basics

  • Combinatorics: Permutations & Combinations

  • Probability Distributions: Binomial, Normal, Poisson

  • Inferential Statistics

    • Confidence Intervals

    • Hypothesis Testing: t-test, chi-square test

  • Correlation vs. Causation

  • Central Limit Theorem

  • ANOVA and Regression Basics

Lab: Exploratory Data Analysis using pandas, matplotlib, and stats methods

Module 4: Advanced Python for Data Science (15 Hours)

Objective: Master libraries, tools, and efficient coding practices.

Topics:

  • Working with NumPy: arrays, slicing, broadcasting

  • Data Analysis with pandas: Series, DataFrames, indexing, groupby

  • Data Visualization:

    • Matplotlib and Seaborn

    • Plot types, customization

  • List Comprehensions & Lambda Functions

  • Iterators, Generators

  • Working with APIs & JSON

  • Introduction to Web Scraping (with requests & BeautifulSoup)

  • Virtual Environments & Packages (venv, pip, conda)

Lab: Real-world EDA Project – Cleaning, transforming, and visualizing a dataset

Module 5: Machine Learning (30 Hours)

Objective: Learn core ML concepts and apply them on real datasets.

 Part 1: Introduction & Supervised Learning

  • ML Workflow Overview

  • Types of ML: Supervised, Unsupervised, Reinforcement

  • Data Preprocessing (scaling, encoding, missing values)

  • Splitting Data & Evaluation Metrics

  • Regression

    • Linear Regression

    • Ridge, Lasso

  • Classification

    • Logistic Regression

    • Decision Trees

    • Random Forests

    • K-Nearest Neighbors

    • Support Vector Machines (SVM)

Mini Project: House Price Prediction using Regression

 Part 2: Unsupervised Learning & Model Optimization

  • Clustering

    • K-Means

    • Hierarchical Clustering

  • Dimensionality Reduction

    • PCA

  • Introduction to Recommender Systems

  • Feature Engineering Techniques

  • Model Validation & Cross-Validation

  • Hyperparameter Tuning (Grid Search, Random Search)

Mini Project: Customer Segmentation using K-Means

Part 3: Capstone Project

  • Guided end-to-end Data Science Project:

    • Problem Statement

    • Data Collection

    • EDA & Feature Engineering

    • Model Building & Evaluation

    • Business Insights

  • Presentation Preparation

Examples:

  • Loan Default Prediction

  • Fraud Detection

  • Movie Recommendation System

Course Add-ons

✅ Books for the course will be shared

✅ Udemy courses from the author - lifetime access

✅ Interview Preparation Tips

✅Access to LMS with class recording

✅ GitHub Portfolio Setup



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