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


In this chapter, we learned about ML and some of the most popular ML algorithms. The primary goal of ML is to build an analytical model using historical data without much human intervention. ML algorithms can be divided into two categories, namely, supervised learning and unsupervised learning. The supervised learning algorithm relies on labeled data to build models, whereas unsupervised learning uses data that is not labeled. We looked at the k-means cluster analysis algorithm, which is an unsupervised ML algorithm. Of the supervised ML algorithms, we explored decision trees, random forests, and ridge/lasso regression. We also got an overview of using NLP for performing text data analysis.

In the next chapter, we will examine the processing of data in real time and perform data analysis as the data becomes available.