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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google, - GOOD
US $9.99
Approximately£7.85
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Located in: Washington, Pennsylvania, United States
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Estimated between Mon, 24 Jun and Wed, 26 Jun to 43230
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eBay item number:335342357860
Item specifics
- Condition
- 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
- Programming / General, Systems Architecture / Distributed Systems & Computing, Web / Social Media, Databases / Data Mining
- Publication Year
- 2019
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 1.1 in
- 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
Publication Name
Mining the Social Web : Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and more
Language
English
Publication Year
2019
Subject
Programming / General, Systems Architecture / Distributed Systems & Computing, Web / Social Media, Databases / Data Mining
Type
Textbook
Subject Area
Computers
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
ebay_catalog_id
4
Copyright Date
2017
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:335342357860
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Item location:
Washington, Pennsylvania, United States
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| Postage and packaging | To | Service | Delivery*See Delivery notes |
|---|---|---|---|
| Free postage | United States | Economy Delivery (USPS Media MailTM) | Estimated between Mon, 24 Jun and Wed, 26 Jun to 43230 |
| Dispatch time |
|---|
| Will usually dispatch within 2 working days of receiving cleared payment. |
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The book was described in new condition. The book I received had a $3.99 goodwill sticker on the back cover along with dog ears, folded pages throughout the back portion of the book. The access codes were sealed as described but the book condition was erroneous. The seller accepted my return, however, I had to go thru return process and refund process, shipping it back, printing a label, while reordering another book from a different seller this time from Amazon, its just easier .
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Shipped quickly and was well packaged. Description was accurate as to quality. Wouldn't hesitate to buy from this seller again.
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