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Data Science from Scratch: First Principles - Paperback, by Grus Joel - New h
US $28.85
ApproximatelyRM 120.81
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eBay item number:405909749380
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Data Science from Scratch: First Principles with Python
- ISBN
- 9781492041139
About this product
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
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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