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The MIT Press Essential Knowledge Ser.: Data Science by Brendan Tierney and John

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eBay item number:226034099919

Item specifics

Condition
Like New: A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is ...
Subject
Science
ISBN
9780262535434

About this product

Product Identifiers

Publisher
MIT Press
ISBN-10
0262535432
ISBN-13
9780262535434
eBay Product ID (ePID)
239611721

Product Key Features

Number of Pages
280 Pages
Language
English
Publication Name
Data Science
Subject
General, Research, Data Processing, Databases / Data Mining
Publication Year
2018
Type
Not Available
Subject Area
Computers, Technology & Engineering, Reference
Author
Brendan Tierney, John D. Kelleher
Series
The MIT Press Essential Knowledge Ser.
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Weight
9 Oz
Item Length
6.9 in
Item Width
5 in

Additional Product Features

LCCN
2017-043665
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.7
Intended Audience
Trade
Synopsis
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
LC Classification Number
QA76.9.B45K45 2018

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