Python Data Science cover

Python Data Science: From Foundations to Applications

An interactive Jupyter-notebook textbook covering the complete modern data-science workflow in Python — from first programming principles through numerical computing, statistics, machine learning, deep learning, and real-world business and technology applications. Every chapter includes learning objectives, executed code with real outputs, a case study, and exercises.

🛡️ Protected edition. Right-click, text selection, image dragging, and common copy / developer-tools shortcuts are disabled. Each chapter is a self-contained page with embedded figures and tables.

6 parts · 27 chapters · self-contained pages with embedded figures and tables.

Python Foundations

  1. Chapter 1 — Introduction to Python and the Data Science Workflow
  2. Chapter 2 — Python Basics: Variables, Types, Operators, and I/O
  3. Chapter 3 — Control Flow: Selections and Loops
  4. Chapter 4 — Functions, Modules, and Scope
  5. Chapter 5 — Core Data Structures: Lists, Tuples, Sets, Dictionaries
  6. Chapter 6 — Strings, Files, and Object-Oriented Programming

Data Science Core

  1. Chapter 7 — Numerical Computing with NumPy
  2. Chapter 8 — Data Manipulation with Pandas
  3. Chapter 9 — Data Visualization with Matplotlib, Seaborn, and Plotly
  4. Chapter 10 — Data Cleaning and Preparation
  5. Chapter 11 — Exploratory Data Analysis

Math Stats Foundations

  1. Chapter 12 — Linear Algebra and Calculus for Data Science
  2. Chapter 13 — Probability and Statistics
  3. Chapter 14 — Statistical Inference and Hypothesis Testing

Machine Learning

  1. Chapter 15 — Introduction to Machine Learning with Scikit-Learn
  2. Chapter 16 — Regression Analysis
  3. Chapter 17 — Classification
  4. Chapter 18 — Unsupervised Learning and Clustering
  5. Chapter 19 — Model Evaluation, Tuning, and Ensemble Methods

Advanced Topics

  1. Chapter 20 — Deep Learning with TensorFlow/Keras
  2. Chapter 21 — Natural Language Processing
  3. Chapter 22 — Time Series Analysis and Forecasting
  4. Chapter 23 — Recommender Systems
  5. Chapter 24 — Big Data Concepts, SQL, and Distributed Computing

Applications

  1. Chapter 25 — Business Analytics and KPI Dashboards
  2. Chapter 26 — Financial, Marketing, and Customer Analytics
  3. Chapter 27 — Data Science Ethics, Privacy, and Responsible AI

Python Data Science: From Foundations to Applications — Protected Edition