Chapters
Chapter 1

Getting Started

Installation, IDEs, and Your First Script

Set up your Python environment with Anaconda or pip, choose between Jupyter Notebook and VS Code, and write your first program using variables and print().

Core
Chapter 2

Data Types & Control Flow

Variables, Collections, Loops, and Conditionals

Master Python's built-in data types (int, float, str, list, dict, tuple) and control structures including if/elif/else, for/while loops, and list comprehensions.

Core
Chapter 3

Functions & Modules

Defining, Importing, and Packaging Code

Write reusable functions with def, work with args/kwargs and lambda expressions, import standard library modules, and install third-party packages with pip.

Core
Chapter 4

NumPy: Arrays & Numerical Computing

Vectorized Operations and Linear Algebra

Create and manipulate NumPy arrays with broadcasting, indexing, and vectorized operations. Apply random number generation and linear algebra routines to analytics problems.

Data
Chapter 5

Pandas: DataFrames & Data Wrangling

Loading, Cleaning, Grouping, and Merging Data

Read CSV and Excel files into DataFrames, select data with loc/iloc, group and aggregate with groupby, merge datasets, create pivot tables, and handle missing values.

Data
Chapter 6

Data Visualization

Matplotlib and Seaborn

Build publication-quality plots with Matplotlib (line, scatter, bar, histogram, subplots) and Seaborn (heatmaps, pairplots), with styling and customization techniques.

Visualization
Chapter 7

Statistical Analysis

scipy and statsmodels

Run hypothesis tests (t-test, chi-square, ANOVA), fit OLS regression models with statsmodels, interpret diagnostics, and compute confidence intervals for business decisions.

Data Core
Chapter 8

Machine Learning Basics

scikit-learn

Split data for training and evaluation, fit linear and logistic regression, decision trees, evaluate with accuracy/RMSE/AUC, and build pipelines with cross-validation.

ML
Chapter 9

Working with APIs & Web Data

requests, JSON, and BeautifulSoup

Fetch data from REST APIs with the requests library, parse JSON responses, read HTML tables with pandas, and perform basic web scraping with BeautifulSoup.

Data
Chapter 10

Project: End-to-End Data Pipeline

Capstone Workflow from Raw Data to Report

Build a complete analytics workflow: load CSV data, clean and explore, engineer features, train and evaluate a model, and generate a summary report tying together all previous chapters.

Capstone
Official Resources

Python Documentation

Learning Platforms

Key Libraries