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

Sampling data

To explore large datasets, it is generally useful to work with a smaller sample of data first. For example, from a dataset consisting of 100 million records, we could take a sample of 1,000 records and start exploring some important properties of this data. Exploring the entire dataset would be ideal; however, the time required to do so would increase manifold.

Selecting the sample

For working with samples, it is important that sample selection is done carefully and biases are not introduced unnecessarily. Randomness plays a very important role in this.

Let's look at how we can make use of the Scala collection API to select sample data from a dataset:

  1. Create a list of 1000 numbers using Scala's Range...