Book description
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these 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 the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
 Get a crash course in Python
 Learn the basics of linear algebra, statistics, and probability—and 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 knearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
 Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Publisher resources
Table of contents
 Preface to the Second Edition
 Preface to the First Edition
 1. Introduction

2. A Crash Course in Python
 The Zen of Python
 Getting Python
 Virtual Environments
 Whitespace Formatting
 Modules
 Functions
 Strings
 Exceptions
 Lists
 Tuples
 Dictionaries
 Counters
 Sets
 Control Flow
 Truthiness
 Sorting
 List Comprehensions
 Automated Testing and assert
 ObjectOriented Programming
 Iterables and Generators
 Randomness
 Regular Expressions
 Functional Programming
 zip and Argument Unpacking
 args and kwargs
 Type Annotations
 Welcome to DataSciencester!
 For Further Exploration
 3. Visualizing Data
 4. Linear Algebra
 5. Statistics
 6. Probability
 7. Hypothesis and Inference
 8. Gradient Descent
 9. Getting Data
 10. Working with Data
 11. Machine Learning
 12. kNearest Neighbors
 13. Naive Bayes
 14. Simple Linear Regression
 15. Multiple Regression
 16. Logistic Regression
 17. Decision Trees
 18. Neural Networks
 19. Deep Learning
 20. Clustering
 21. Natural Language Processing
 22. Network Analysis
 23. Recommender Systems
 24. Databases and SQL
 25. MapReduce
 26. Data Ethics
 27. Go Forth and Do Data Science
 Index
Product information
 Title: Data Science from Scratch, 2nd Edition
 Author(s):
 Release date: May 2019
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781492041139
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