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)
Free Chapter
Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Traditional Machine Learning for Data Analysis

This chapter provides an overview of machine learning (ML) techniques for doing data analysis. In the previous chapters, we have explored some of the techniques that can be used by human beings to analyze and understand data. In this chapter, we look at how ML techniques could be used for similar purposes.

At the heart of ML is a number of algorithms that have proven to work for solving specific categories of problems with a high degree of effectiveness. This chapter covers the following popular ML methods:

  • Decision trees
  • Random forests
  • Ridge and lasso regression
  • k-means cluster analysis

It also covers the role of natural language processing (NLP) in effectively analyzing certain types of data problems. The discussion in this chapter is limited to traditional machine learning methods. It does not cover newer methods such as deep...