On November 8, 2016, American citizens went in millions to polling stations to cast their votes for the next President of the United States. Counting began almost immediately and, although not officially confirmed until sometime later, the forecasted result was well known by the next morning. Let's start our investigation a couple of days before the major event itself, on November 6, 2016, so that we can preserve some context in the run-up. Although we do not exactly know what we will find in advance, we know that Twitter will play an oversized role in the political commentary given its influence in the build-up, and it makes sense to start collecting data as soon as possible. In fact, data scientists may sometimes experience this as a gut feeling - a strange and often exciting notion that compels us to commence working on something without a clear plan or absolute justification, just a sense that it will pay off. And actually, this approach can be vital...
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