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Hands-On Data Analysis with Scala
Hands-On Data Analysis with Scala

Hands-On Data Analysis with Scala: Perform data collection, processing, manipulation, and visualization with Scala

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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
Paperback May 2019 298 pages 1st Edition
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$24.99 $35.99
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Arrow left icon
Profile Icon Gupta Profile Icon Gupta
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
Paperback May 2019 298 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$48.99
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Renews at $19.99p/m

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Hands-On Data Analysis with Scala

Scala Overview

Scala is a popular general-purpose, high-level programming language that typically runs on the Java Virtual Machine (JVM). JVM is a time-tested platform that has proven itself in terms of stability and performance. A large number of very powerful libraries and frameworks have been built using Java. For instance, in the context of data analysis, there are many Java libraries available to handle different data formats, such as XML, JSON, Avro, and so on. Scala's interoperability with such well-tested libraries helps increase a Scala programmer's productivity greatly.

When it comes to data analysis and processing, it is often the case that there is an abundance of data transformation tasks that need to be performed. Some examples of such tasks are mapping from one representation to another, filtering irrelevant data, and joining one set of data with another...

Getting started with Scala

At the time of writing, Scala Version 2.12.8 (https://www.scala-lang.org/) is the most recent version of the language. We have the option of running Scala code online using our browser or installing and running it on our machine. Running it online is good for getting started with Scala as a first step; however, you will need to install it on your computer to learn the language's in-depth features and make use of it for data analysis.

Running Scala code online

There are some great resources available online to run code in your web browser. These are good for trying out small snippets of Scala code and gaining a better understanding of how Scala works. Some of these online resources allow you...

Overview of object-oriented and functional programming

Scala supports object-oriented and functional styles of programming. Both of these programming paradigms have been proven to help model and solve real-world problems. In this section, we will explore both of these styles of programming using Scala.

Object-oriented programming using Scala

In the object-oriented paradigm, you think in terms of objects and classes. A class can be thought of as a template that acts as a basis for creating objects of that type. For example, a Vehicle class can represent real-world automobiles with the following attributes:

  • vin (a unique vehicle identification number)
  • manufacturer
  • model
  • modelYear
  • finalAssemblyCountry

A concrete instance of...

Scala case classes and the collection API

Scala case classes and its collection API play a significant role in data analysis using Scala. This section will give you insight into these topics and an understanding of their relevance in the context of data analysis.

During the data analysis process, we will frequently encounter data that consists of a collection of records. These records often need to be transformed, cleaned, or filtered.

Scala case classes

Scala case classes provide a convenient mechanism to work with objects that hold values. Let's look at an example in Scala REPL. The case class defined in the following code will be used in other example codes in this chapter:

scala> case class Person(fname: String...

Overview of Scala libraries for data analysis

There are a great number of Scala libraries and frameworks that simplify data analysis tasks. There is a lot of innovation happening regarding the simplification of data analysis-related tasks, from simple tasks such as data cleaning, to more advanced tasks such as deep learning. The following sections focus on the most popular data-centric libraries and frameworks that have seamless Scala integration.

Apache Spark

Apache Spark (https://spark.apache.org/) is a unified analytics engine for large-scale data processing. Spark provides APIs for batch as well as stream data processing in a distributed computing environment. Spark's API can be broadly divided into the following...

Summary

This chapter provided a high-level overview of the Scala programming language. We looked at some of the object-oriented and functional programming aspects of Scala using applied examples. This chapter touched upon the array, list, and map functionalities of the Scala collection API. These data structures have numerous uses in the data analysis life cycle. The chapter also provided the necessary information to set up and install the Scala tools that are essential for understanding and applying the topics covered in subsequent chapters. Finally, a quick overview of the data-centric Scala libraries was provided. We will be making use of these libraries in the next few chapters to solve specific data life cycle problems.

In the next chapter, we will look at the data analysis life cycle and associated tasks.

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Key benefits

  • A beginner's guide for performing data analysis loaded with numerous rich, practical examples
  • Access to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysis
  • Develop applications in Scala for real-time analysis and machine learning in Apache Spark

Description

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

Who is this book for?

If you are a data scientist or a data analyst who wants to learn how to perform data analysis using Scala, this book is for you. All you need is knowledge of the basic fundamentals of Scala programming.

What you will learn

  • Techniques to determine the validity and confidence level of data
  • Apply quartiles and n-tiles to datasets to see how data is distributed into many buckets
  • Create data pipelines that combine multiple data lifecycle steps
  • Use built-in features to gain a deeper understanding of the data
  • Apply Lasso regression analysis method to your data
  • Compare Apache Spark API with traditional Apache Spark data analysis

Product Details

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Publication date : May 03, 2019
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346114
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Product Details

Publication date : May 03, 2019
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346114
Category :
Languages :
Concepts :

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Frequently bought together


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Machine Learning with Scala Quick Start Guide
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Hands-On Data Analysis with Scala
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Table of Contents

13 Chapters
Section 1: Scala and Data Analysis Life Cycle Chevron down icon Chevron up icon
Scala Overview Chevron down icon Chevron up icon
Data Analysis Life Cycle Chevron down icon Chevron up icon
Data Ingestion Chevron down icon Chevron up icon
Data Exploration and Visualization Chevron down icon Chevron up icon
Applying Statistics and Hypothesis Testing Chevron down icon Chevron up icon
Section 2: Advanced Data Analysis and Machine Learning Chevron down icon Chevron up icon
Introduction to Spark for Distributed Data Analysis Chevron down icon Chevron up icon
Traditional Machine Learning for Data Analysis Chevron down icon Chevron up icon
Section 3: Real-Time Data Analysis and Scalability Chevron down icon Chevron up icon
Near Real-Time Data Analysis Using Streaming Chevron down icon Chevron up icon
Working with Data at Scale Chevron down icon Chevron up icon
Another Book You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
bindu Jul 22, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very Resourceful book for learning Scala. Detailed explanation right from the setup. Each topic is well explained with UML diagrams followed by practical examples. This book even covers Spark and Machine Learning Core Concepts.
Amazon Verified review Amazon
Sandy Jul 28, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great read, to quickly come up to speed on Data Analysis with Scala. The author explains everything in very simple terms and remove need of any prerequisites . It is must read for anybody interested on the subject. It doesn't waste anytime in theoretical concepts and jumps directly on the subject with step by step instructions with lot of examples. The contents and the style of book reflects author experience on the subject.
Amazon Verified review Amazon
M. Sharma Aug 14, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have been thinking about writing the review for this book for some time.I was planning to switch from R/SAS to Scala for data analysis and ML project on Spark.I was looking for a book which is quick read and cover various practical examples of using Scala with large datasets. Then I came across this book. It covers basics of language and shows how to use Scala/Spark for practical projects. It helped me to setup Scala and various tools and library on Spark cluster.Book is very methodical in its approach covering various aspects of big data analytics projects. It helped me to be productive in no time and start using Scala and Spark.One can finish this book in short period of time and will be ready to work on large scale Scala projects.Author has done great justice to the title and I highly recommend this work to anyone who wants to learn and adapt Scala/Spark.
Amazon Verified review Amazon
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