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Social Media Data Mining and Analytics
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From the Back Cover
LEARN TO MINE SOCIAL MEDIA DATA FOR A COMPETITIVE EDGE Social media is a rich source of big data, so much so that 90% of Fortune 500 companies are investing in big data initiatives to help them predict consumer behavior. Knowing the most effective ways to mine social media data can help you acquire information that generates amazing business results. Social media is unstructured, dynamic, and future-oriented. Effective, insightful data mining requires new analytical tools and techniques. Written by experts at social networking companies, Social Media Data Mining and Analytics provides a hands-on course that teaches you how to use state-of-the-art tools and sophisticated data mining techniques specifically geared to social media. It digs deeply into the mechanics of collecting and applying social media data to understand customers, define trends, and make predictions that can improve analytics for growth and sales. You will discover how to make the most of data gathered from social media and other related rich data sources. You'll learn how to identify common patterns of online user behavior so you can independently build and apply predictive algorithms that exploit these patterns. Social Media Data Mining and Analytics will teach you: The four key characteristics of online services: users, social networks, actions, and content The data discovery lifecycle: data extraction, analysis, and visualization Techniques for using social media to make customer predictions and recommendations How to use distributed computing to efficiently process large amounts of social media data Solutions using code-level examples written in Python, R, and Scala
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About the Author
GABOR SZABO, PHD, is a Senior Staff Software Engineer at Tesla and a former data scientist at Twitter, where he focused on predicting user behavior and content popularity in crowdsourced online services, and on modeling large-scale content dynamics. He also authored the PyCascading data processing library. GUNGOR POLATKAN, PHD, is a Tech Lead/Engineering Manager designing and implementing end-to-end machine learning and artificial intelligence offline/online pipelines for the LinkedIn Learning relevance backend. He was previously a machine learning scientist at Twitter, where he worked on topics such as ad targeting and user modeling. P. OSCAR BOYKIN, PHD, is a software engineer at Stripe where he works on machine learning infrastructure. He was previously a Senior Staff Engineer at Twitter, where he worked on data infrastructure problems. He is coauthor of the Scala big-data libraries Algebird, Scalding and Summingbird. ANTONIOS CHALKIOPOULOS, MSC, is a Distributed Systems Specialist. A system engineer who has delivered fast/big data projects in media, betting, and finance, he is now leading the effort on the Lenses platform for data streaming as a co-founder and CEO at https://lenses.stream.
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Product details
Paperback: 352 pages
Publisher: Wiley; 1 edition (October 23, 2018)
Language: English
ISBN-10: 1118824857
ISBN-13: 978-1118824856
Product Dimensions:
7.4 x 0.8 x 9.1 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
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Amazon Best Sellers Rank:
#929,302 in Books (See Top 100 in Books)
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