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Data Science from Scratch : First Principles with Python by Joel Grus (2019, Trade Paperback)
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101492041130
ISBN-139781492041139
eBay Product ID (ePID)5038681800
Product Key Features
Number of Pages403 Pages
LanguageEnglish
Publication NameData Science from Scratch : First Principles with Python
Publication Year2019
SubjectData Modeling & Design, Databases / Data Mining, Statistics, Programming Languages / Python, Business Mathematics
TypeTextbook
AuthorJoel Grus
Subject AreaComputers, Business & Economics
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight24.9 Oz
Item Length9.1 in
Item Width7.4 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
Dewey Edition23
IllustratedYes
Dewey Decimal006.312
SynopsisTo 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, 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, Na ve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
LC Classification NumberQA76.9.D343
