Hands-On Data Analysis With Scala by Gupta, Rajesh -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On Data Analysis with Scala

Dedication About Packt

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Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

Get in touch

Reviews

Section 1: Scala and Data Analysis Life Cycle Scala Overview

Getting started with Scala

Running Scala code online

Scastie ScalaFiddle

Installing Scala on your computer

Installing command-line tools Installing IDE

Overview of object-oriented and functional programming

Object-oriented programming using Scala Functional programming using Scala

Scala case classes and the collection API

Scala case classes Scala collection API

Array List Map

Overview of Scala libraries for data analysis

Apache Spark Breeze Breeze-viz DeepLearning Epic Saddle Scalalab Smile Vegas

Summary

Data Analysis Life Cycle

Data journey Sourcing data

Data formats

XML JSON CSV

Understanding data

Using statistical methods for data exploration

Using Scala Other Scala tools

Using data visualization for data exploration

Using the vegas-viz library for data visualization Other libraries for data visualization

Using ML to learn from data

Setting up Smile Running Smile

Creating a data pipeline Summary

Data Ingestion

Data extraction

Pull-oriented data extraction Push-oriented data delivery

Data staging

Why is the staging important?

Cleaning and normalizing Enriching Organizing and storing Summary

Data Exploration and Visualization

Sampling data

Selecting the sample

Selecting samples using Saddle

Performing ad hoc analysis Finding a relationship between data elements Visualizing data

Vegas viz for data visualization Spark Notebook for data visualization

Downloading and installing Spark Notebook Creating a Spark Notebook with simple visuals More charts with Spark Notebook

Box plot Histogram Bubble chart

Summary

Applying Statistics and Hypothesis Testing

Basics of statistics

Summary level statistics Correlation statistics

Vector level statistics Random data generation

Pseudorandom numbers Random numbers with normal distribution Random numbers with Poisson distribution

Hypothesis testing Summary

Section 2: Advanced Data Analysis and Machine Learning Introduction to Spark for Distributed Data Analysis

Spark setup and overview

Spark core concepts

Spark Datasets and DataFrames Sourcing data using Spark

Parquet file format Avro file format Spark JDBC integration

Using Spark to explore data Summary

Traditional Machine Learning for Data Analysis

ML overview

Characteristics of ML Categories or types of ML

Decision trees

Implementing decision trees

Decision tree algorithms

Implementing decision tree algorithms in our example Evaluating the results

Using our model with a decision tree

Random forest

Random forest algorithms

Ridge and lasso regression

Characteristics of ridge regression Characteristics of 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

Word count using pure Scala Word count using Scala and Spark Word count using Scala and Spark Streaming Deep dive into the Spark Streaming solution

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

Data storage Data governance

Reliability considerations

Input data errors Processing failures

Summary

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