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

ML overview

Let's first look at what ML is. In a traditional sense, in order to solve a computational problem, we typically write explicit computer instructions that solve the problem based on all of the possible scenarios. The assumption here is that all of the rules associated with the specific problem being solved are known and well-defined in advance and could be codified into computer instructions. This assumption, however, is not always true. There are times when the rules are not known in advance and it is impractical to define deterministic rules that could be applied to solve the problem.

Let's look at this problem using a concrete example of an app stores where a consumer has the option of buying an app from a fairly large catalog of available apps. When the consumer logs into the app store, it displays a set of recommended apps that the consumer is highly...