Data Science from Scratch: First Principles with PythonData 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 knowhow to dig those answers out.

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User Review  OstkUser894211  Overstock.comWellwritten easy to follow. Book arrived very slightly damaged through rough handling during shipping. Read full review
Contents
Chapter 1 Introduction  1 
Chapter 2 A Crash Course in Python  15 
Chapter 3 Visualizing Data  37 
Chapter 4 Linear Algebra  49 
Chapter 5 Statistics  57 
Chapter 6 Probability  69 
Chapter 7 Hypothesis and Inference  81 
Chapter 8 Gradient Descent  93 
Chapter 15 Multiple Regression  179 
Chapter 16 Logistic Regression  189 
Chapter 17 Decision Trees  201 
Chapter 18 Neural Networks  213 
Chapter 19 Clustering  225 
Chapter 20 Natural Language Processing  239 
Chapter 21 Network Analysis  255 
Chapter 22 Recommender Systems  267 
Chapter 9 Getting Data  103 
Chapter 10 Working with Data  121 
Chapter 11 Machine Learning  141 
Chapter 12 kNearest Neighbors  151 
Chapter 13 Naive Bayes  165 
Chapter 14 Simple Linear Regression  173 