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

Data Exploration and Visualization

Data exploration is about trying to gain an understanding of patterns and relationships hidden inside the data. Data visualization helps tremendously in this process. In fact, visual methods are frequently used to explain and communicate these patterns and relationships to an interested audience. It needs to be noted that data exploratory analysis and data explanatory analysis are two different things. Data explanatory analysis can only start after data exploratory analysis is completed. Our focus here is primarily data exploratory analysis and we want to discover and learn about the structure of data. Visual tools play a more dominant role in explanatory data analysis; however, these also play an equally important role during data exploration.

The following are the topics that we will be covering in this chapter:

  • Sampling data
  • Performing ad...