Data Science From Scratch by Joel Grus provides a crash course on all you need to know to establish a data science program. It covers data science frameworks, libraries, code, modules, toolkits and the many approaches to leveraging them. More importantly, it teaches the most fundamental data science tools and algorithms and shows how to work with them by scratch, from the very beginning.
If you have an aptitude for mathematics and some programming skills, this book 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.
- 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
For more info and to get your book see: Learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
