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.
With this updated second edition, 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.
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.
About the Author:
Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours.
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. Deep Learning
Chapter 20. Clustering
Chapter 21. Natural Language Processing
Chapter 22. Network Analysis
Chapter 23. Recommender Systems
Chapter 24. Databases and SQL
Chapter 25. MapReduce
Chapter 26. Data Ethics
Chapter 27. Go Forth and Do Data Science
About the book:
Publisher : O'Reilly Media; 2nd edition (May 16, 2019)
Language : English
Paperback : 513 pages
Item Weight : 1.4 pounds
Dimensions : 6.9 x 0.9 x 9.1 inches
File: PDF, 10MB
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