Spark for Data Science

Key Features

• Perform data analysis and build predictive models on huge datasets that leverage Apache Spark
• Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges
• Work through practical examples on real-world problems with sample code snippets

Book Description

This is the era of Big Data and Internet of Things! Big Data implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.

Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.

With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.

What you will learn

• Consolidate, clean, and transform your data acquired from various data sources
• Perform statistical analysis of data to find hidden insights
• Explore graphical techniques to see what your data looks like
• Use machine learning techniques to build predictive models
• Build scalable data products and solutions
• Start programming using the RADD API
• Become an expert by improving your data analytical skills

About the Author

Bikramaditya Singhal works as a Senior Data Science Analyst with Broadridge Financial Solutions (India) Pvt. Ltd. He has over 6 years of experience in statistical analysis, machine learning, and also in developing, designing, and architecting data-driven solutions.

His passion for technology and applied mathematics propelled him to pursue a career in data science. He is a strong believer in continuous innovation. He worked with Microsoft India and cofounded a company that provides data-driven insights to clients globally.

He has been a speaker at various conferences and meetups on data science, machine learning, and Apache Spark. His current skillset includes statistical data analysis, machine learning, R, Python, Scala, and ETL tools. With a unique blend of science as well as the technology aspect of Big Data, he has been instrumental in providing solutions to Big Data analytics problems.

Srinivas Duvvuri is currently heading the Fixed Income Suite of products at Broadridge India, and is also a principal member of the Broadridge Technology Council. In addition, he is involved in setting up the Big Data COE at Broadridge. He has over 22 years of experience in software product development and engineering complex, high-performance, scalable, multi-platform software solutions based on cutting edge technologies.

His experience predominantly spans product development in multiple domains including financial services, infrastructure management, OLAP, telecom billing, and customer care. Prior to Broadridge, he held leadership positions at a start-up and at leading IT majors such as CA, Hyperion (Oracle), and Globalstar, and also has a patent in Relational OLAP. Srinivas has a B.Tech in Aeronautics Engineering and an M.Tech in Computer Science, from IIT, Madras.

Table of Contents

Chapter 1: Big Data and Data Science – An Introduction
Chapter 2: The Spark Programming Model
Chapter 3: Introduction to DataFrames
Chapter 4: Unified Data Access
Data Analysis on Chapter 5: Spark
Chapter 6: Machine Learning
Chapter 7: Extending Spark with SparkR
Chapter 8: Analyzing Unstructured Data
Chapter 9: Visualizing Big Data
Chapter 10: Putting It All Together
Chapter 11: Building Data Science Applications