Data science from scratch : first principles with Python
Published
Sebastopol, CA : O'Reilly Media, [2019].
Appears on list
Status
Available Online
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
More Details
Format
Edition
Second edition.
Language
English
ISBN
9781492041108, 1492041106, 9781492041085, 1492041084
Notes
Bibliography
Includes bibliographical references and index.
Description
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
Table of Contents
Introduction
A crash course in Python
Visualizing data
Linear algebra
Statistics
Probability
Hypothesis and inference
Gradient descent
Getting data
Working with data
Machine learning
k-Nearest neighbors
Naive bayes
Simple linear regression
Multiple regression
Logistic regression
Decision trees
Neural networks
Deep learning
Clustering
Natural language processing
Network analysis
Recommender systems
Databases and SQL
MapReduce
Data ethics
Go forth and do data science.
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Citations
APA Citation, 7th Edition (style guide)
Grus, J. (. e. (2019). Data science from scratch: first principles with Python (Second edition.). O'Reilly Media.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Grus, Joel (Software engineer). 2019. Data Science From Scratch: First Principles With Python. Sebastopol, CA: O'Reilly Media.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Grus, Joel (Software engineer). Data Science From Scratch: First Principles With Python Sebastopol, CA: O'Reilly Media, 2019.
Harvard Citation (style guide)Grus, J. (. e. (2019). Data science from scratch: first principles with python. Second edn. Sebastopol, CA: O'Reilly Media.
MLA Citation, 9th Edition (style guide)Grus, Joel (Software engineer). Data Science From Scratch: First Principles With Python Second edition., O'Reilly Media, 2019.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
9c03fa02-967e-fe58-8b31-0754a87a269a-eng
Grouping Information
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| Last Update | 2025-05-15 14:23:11PM |
| Last Indexed | 2025-05-22 03:29:58AM |
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