Book Image

Spark for Data Science

By : Srinivas Duvvuri, Bikramaditya Singhal
Book Image

Spark for Data Science

By: Srinivas Duvvuri, Bikramaditya Singhal

Overview of this book

This is the era of Big Data. The words ‘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.
Table of Contents (18 chapters)
Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Introduction


Machine learning is all about learning by example data; examples that produce a particular output for a given input. There are various business use cases for machine learning. Let us look at a few examples to get an idea of what exactly it is:

  • A recommendation engine that recommends users what they might be interested in buying

  • Customer segmentation (grouping customers who share similar characteristics) for marketing campaigns

  • Disease classification for cancer - malignant/benign

  • Predictive modeling, for example, sales forecasting, weather forecasting

  • Drawing business inferences, for example, understanding what effect will change the price of a product have on sales

The evolution

The concept of statistical learning was existent even before the first computer system was introduced. In the nineteenth century, the least squares technique (now called linear regression) had already been developed. For classification problems, Fisher came up with Linear Discriminant Analysis (LDA). Around...