What makes data science different from statistics is the emphasis on scalable processing to overcome complex issues surrounding the quality and variety of the collected data. While statisticians work on samples of clean datasets, perhaps coming from a relational database, data scientists in contrast, work at scale with unstructured data coming from a variety of sources. While the former focuses on building models having high degrees of precision and accuracy, the latter often focuses on constructing rich integrated datasets that offer the discovery of less strictly defined insights. The data science journey usually involves torturing the initial sources of data, joining datasets that were theoretically not meant to be joined, enriching content with publicly available information, experimenting, exploring, discovering, trying, failing, and trying again. No matter the technical or mathematical skills, the main difference between an average and an expert data...
Mastering Spark for Data Science
By :
Mastering Spark for Data Science
By:
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.
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