Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

• Get a crash course in Python
• Learn the basics of linear algebra, statistics, and probability—and understand how and when they’re used in data science
• Collect, explore, clean, munge, and manipulate data
• Dive into the fundamentals of machine learning
• Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
• Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Table of Contents

Chapter 1. Introduction
Chapter 2. A Crash Course in Python
Chapter 3. Visualizing Data
Chapter 4. Linear Algebra
Chapter 5. Statistics
Chapter 6. Probability
Chapter 7. Hypothesis and Inference
Chapter 8. Gradient Descent
Chapter 9. Getting Data
Chapter 10. Working with Data
Chapter 11. Machine Learning
Chapter 12. k-Nearest Neighbors
Chapter 13. Naive Bayes
Chapter 14. Simple Linear Regression
Chapter 15. Multiple Regression
Chapter 16. Logistic Regression
Chapter 17. Decision Trees
Chapter 18. Neural Networks
Chapter 19. Clustering
Chapter 20. Natural Language Processing
Chapter 21. Network Analysis
Chapter 22. Recommender Systems
Chapter 23. Databases and SQL
Chapter 24. MapReduce
Chapter 25. Go Forth and Do Data Science