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Hands-On Data Analysis with Scala

By : Gupta

5 (3)

Buy this Book

Hands-On Data Analysis with Scala

5 (3)

By: Gupta

Buy this Book

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

Preface

Who this book is for

What this book covers

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Section 1: Scala and Data Analysis Life Cycle

Scala Overview

Getting started with Scala

Overview of object-oriented and functional programming

Scala case classes and the collection API

Overview of Scala libraries for data analysis

Summary

Data Analysis Life Cycle

Data journey

Sourcing data

Understanding data

Using ML to learn from data

Creating a data pipeline

Summary

Data Ingestion

Data extraction

Data staging

Cleaning and normalizing

Enriching

Organizing and storing

Summary

Data Exploration and Visualization

Sampling data

Performing ad hoc analysis

Finding a relationship between data elements

Visualizing data

Summary

Applying Statistics and Hypothesis Testing

Basics of statistics

Vector level statistics

Random data generation

Hypothesis testing

Summary

Section 2: Advanced Data Analysis and Machine Learning

Introduction to Spark for Distributed Data Analysis

Spark setup and overview

Spark Datasets and DataFrames

Sourcing data using Spark

Using Spark to explore data

Summary

Traditional Machine Learning for Data Analysis

ML overview

Decision trees

Random forest

Ridge and lasso regression

k-means cluster analysis

Natural language processing for data analysis

Algorithm selections

Summary

Section 3: Real-Time Data Analysis and Scalability

Near Real-Time Data Analysis Using Streaming

Overview of streaming

Spark Streaming overview

Streaming a k-means clustering algorithm using Spark

Streaming linear regression using Spark

Summary

Working with Data at Scale

Working with data at scale

Cost considerations

Reliability considerations

Summary

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Algorithm selections

Each ML algorithm has its own strengths and weaknesses. Selecting an appropriate machine algorithm and tuning the model requires a fair amount of experience working with these algorithms, however, the following factors also play a significant role in applying these techniques effectively:

Asking the right question: A great deal of effort is generally required in formulating the right question.
Understanding the business domain: Having a good understanding of the relevant business domain and context is equally important to build good models.
Understanding data: Ultimately, the data is used to train the model. If the data is not understood correctly or the data quality is poor, the built model is unlikely to be effective.

All of the preceding aspects outlined are somewhat interdependent and a mastery of all of these is a prerequisite to selecting the appropriate...