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Data science from scratch [electronic book] : first principles with Python / Joel Grus.

By: Grus, Joel (Software engineer) [author].
Material type: materialTypeLabelBookPublisher: Sebastopol, CA : O' Reilly Media, Inc., 2019Copyright date: ©2019Edition: Second edition.Description: online resource (xviii, 376 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9781492041139 (paperback); 9781492041108 (e-book).Subject(s): Python (Computer program language) | Data miningAdditional physical formats: Print version: Data science from scratch : first principles with Python DDC classification: 006.312 Online resources: Access eBook Also available in print form.
Contents:
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.
Holdings
Item type Current library Call number Status Notes
eBook MTU Online eBook 006.312 (Browse shelf(Opens below)) Available CIT Module COMP 8060 - Core reading. CIT Module SOFT 8032 - Core reading.
Total holds: 0

Enhanced descriptions from Syndetics:

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

Includes index.

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.

CIT Module COMP 8060 - Core reading.

CIT Module SOFT 8032 - Core reading.

Also available in print form.

Electronic reproduction.: ProQuest LibCentral. Mode of access: World Wide Web.

Author notes provided by Syndetics

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. He blogs infrequently at joelgrus.com and tweets all day long at @joelgrus.