When we look back at the year 2016, we will surely remember it as a time of many significant geo-political events ranging from Brexit, Great Britain's vote to leave the European Union, to the untimely passing of many beloved celebrities, including the sudden death of the singer David Bowie (covered in Chapter 6, Scraping Link-Based External Data and Chapter 7, Building Communities). However, perhaps the most notable occurrence of the year was the tense US presidential election and its eventual outcome, the election of President Donald Trump. A campaign that will long be remembered, not least for its unprecedented use of social media, and the stirring up of passion among its users, most of whom made their feelings known through the use of hashtags: either positive ones, such as #MakeAmericaGreatAgain or #StrongerTogether, or conversely negative ones, such as #DumpTrump or #LockHerUp. Since this chapter is about sentiment analysis, the election...
Mastering Spark for Data Science
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Mastering Spark for Data Science
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Overview of this book
Data science seeks to transform the world using data, and this is typically achieved
through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.
This book deep dives into using Spark to deliver production-grade data science
solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.
You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
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Current Title:
Mastering Spark for Data Science
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
The Big Data Science Ecosystem
Data Acquisition
Input Formats and Schema
Exploratory Data Analysis
Spark for Geographic Analysis
Scraping Link-Based External Data
Building Communities
Building a Recommendation System
News Dictionary and Real-Time Tagging System
Story De-duplication and Mutation
Anomaly Detection on Sentiment Analysis
TrendCalculus
Secure Data
Customer Reviews