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