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Data Science from Scratch, 2nd Edition / Joel Grus.

By: Grus, Joel.
Publisher: [Place of publication not identified] O'Reilly Media, Inc., 2019Description: xvii, 384 pages : illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781492041139; 1492041130.Subject(s): | | | | -- MathematicsGenre/Form: Electronic books. LOC classification: QA76.73.P98 | G78 2019
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.
Summary: 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. In this book, 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. -- Provided by publisher.
List(s) this item appears in: 2020 New Titles | Learning Python | Data Analysis
Item type Current location Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library
Mesa Lab
QA76.73.P98 .G78 2019 1 Checked out 10/08/2021 50583020009308
Total holds: 0

Title from content provider.

Includes bibliographical references and index.

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.

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. In this book, 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. -- Provided by publisher.

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