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Chapters

This textbook is organized into 13 chapters covering 300 concepts in data science with Python.

Chapter Overview

  1. Introduction to Data Science - Foundational concepts including data science workflow, types of data, and measurement scales.

  2. Python Environment and Setup - Python installation, package management, Jupyter notebooks, and IDE configuration.

  3. Python Data Structures - Python native structures and pandas DataFrames for data manipulation.

  4. Data Cleaning and Preprocessing - Handling missing values, outliers, and data transformation techniques.

  5. Data Visualization with Matplotlib - Creating effective visualizations with matplotlib and Seaborn.

  6. Statistical Foundations - Descriptive statistics, distributions, probability, and correlation.

  7. Simple Linear Regression - Regression fundamentals, least squares method, and scikit-learn implementation.

  8. Model Evaluation and Validation - Performance metrics, cross-validation, and bias-variance tradeoff.

  9. Multiple Linear Regression - Multiple predictors, feature selection, and handling categorical variables.

  10. NumPy and Numerical Computing - NumPy arrays, vectorization, and matrix operations.

  11. Non-linear Models and Regularization - Polynomial regression and regularization techniques.

  12. Introduction to Machine Learning - Machine learning paradigms, optimization, and gradient descent.

  13. Neural Networks and PyTorch - Neural network architecture, PyTorch, and capstone projects.

  14. Capstone Project - Working in teams to create a final data science project and presenting to your classmates.

How to Use This Textbook

This textbook is designed to be read sequentially, as each chapter builds upon concepts from previous chapters. The learning graph ensures that prerequisite concepts are always introduced before they are needed. However, if you have prior experience with certain topics, you may skip ahead while referring back to earlier chapters as needed.

Each chapter includes interactive MicroSims to reinforce learning through hands-on exploration.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.