Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Scala Overview

Scala is a popular general-purpose, high-level programming language that typically runs on the Java Virtual Machine (JVM). JVM is a time-tested platform that has proven itself in terms of stability and performance. A large number of very powerful libraries and frameworks have been built using Java. For instance, in the context of data analysis, there are many Java libraries available to handle different data formats, such as XML, JSON, Avro, and so on. Scala's interoperability with such well-tested libraries helps increase a Scala programmer's productivity greatly.

When it comes to data analysis and processing, it is often the case that there is an abundance of data transformation tasks that need to be performed. Some examples of such tasks are mapping from one representation to another, filtering irrelevant data, and joining one set of data with another...