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 k-nearest 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
- Object-Oriented 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. k-Nearest 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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