Data Science from Scratch

Joel Grus

ISBN 10: 1492041130 / ISBN 13: 9781492041139

Verlag: O'Reilly Media, US, 2019

Beschreibung

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.

Bestandsnummer des Verkäufers LU-9781492041139

Bibliografische Details

• Titel: Data Science from Scratch
• Verlag: O'Reilly Media, US
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 1492041130
• ISBN-13: 9781492041139
• Einband: Paperback
• Auflage: 2. Auflage
• Zustand: New

Über diesen Titel

Inhaltsangabe

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

Über die Autorin bzw. den Autor

Joel Grus is a software engineer at Google. Before that he worked as a data scientist at multiple startups. He lives in Seattle, where he regularly attends data science happy hours.