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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google, - GOOD

pghcomputertech
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US $9.99
ApproximatelyEUR 8.97
Condition:
New
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Located in: Washington, Pennsylvania, United States
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Estimated between Wed, 25 Sep and Thu, 26 Sep to 43230
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eBay item number:335342357860

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
Brand
Unbranded
Book Title
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn,
MPN
Does not apply
ISBN
9781491985045
Subject Area
Computers
Publication Name
Mining the Social Web : Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and more
Publisher
O'reilly Media, Incorporated
Item Length
9.1 in
Subject
Systems Architecture / Distributed Systems & Computing, Programming / General, Web / Social Media, Databases / Data Mining
Publication Year
2019
Type
Textbook
Format
Trade Paperback
Language
English
Item Height
1.1 in
Author
Matthew Russell, Mikhail Klassen
Item Weight
20.5 Oz
Item Width
7 in
Number of Pages
426 Pages

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1491985046
ISBN-13
9781491985045
eBay Product ID (ePID)
237690225

Product Key Features

Number of Pages
426 Pages
Language
English
Publication Name
Mining the Social Web : Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and more
Publication Year
2019
Subject
Systems Architecture / Distributed Systems & Computing, Programming / General, Web / Social Media, Databases / Data Mining
Type
Textbook
Subject Area
Computers
Author
Matthew Russell, Mikhail Klassen
Format
Trade Paperback

Dimensions

Item Height
1.1 in
Item Weight
20.5 Oz
Item Length
9.1 in
Item Width
7 in

Additional Product Features

Edition Number
3
Intended Audience
Scholarly & Professional
LCCN
2020-277838
Illustrated
Yes
Synopsis
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media--including who's connecting with whom, what they're talking about, and where they're located--using Python code examples, Jupyter notebooks, or Docker containers. In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. Get a straightforward synopsis of the social web landscape Use Docker to easily run each chapter's example code, packaged as a Jupyter notebook Adapt and contribute to the code's open source GitHub repository Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition Build beautiful data visualizations with Python and JavaScript toolkits
LC Classification Number
QA76.9.D343

Item description from the seller

pghcomputertech

pghcomputertech

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Registered as a private seller
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