Data Science from Scratch: First Principles - Paperback, by Grus Joel - New h

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Características del artículo

Estado
Nuevo: Libro nuevo, sin usar y sin leer, que está en perfecto estado; incluye todas las páginas sin ...
Book Title
Data Science from Scratch: First Principles with Python
ISBN
9781492041139
Categoría

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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1492041130
ISBN-13
9781492041139
eBay Product ID (ePID)
5038681800

Product Key Features

Number of Pages
403 Pages
Language
English
Publication Name
Data Science from Scratch : First Principles with Python
Publication Year
2019
Subject
Data Modeling & Design, Databases / Data Mining, Statistics, Programming Languages / Python, Business Mathematics
Type
Textbook
Subject Area
Computers, Business & Economics
Author
Joel Grus
Format
Trade Paperback

Dimensions

Item Height
1 in
Item Weight
24.5 Oz
Item Length
9.1 in
Item Width
7 in

Additional Product Features

Edition Number
2
Intended Audience
Scholarly & Professional
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.312
Synopsis
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, 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 Number
QA76.9.D343

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