Hands-On Data Analysis with Scala电子书

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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

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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