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

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Scala and Data Analysis Life Cycle
Section 1: Scala and Data Analysis Life Cycle
2
Scala Overview
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
3
Data Analysis Life Cycle
Data Analysis Life Cycle
Data journey
Sourcing data
Understanding data
Using ML to learn from data
Creating a data pipeline
Summary
4
Data Ingestion
Data Ingestion
Data extraction
Data staging
Cleaning and normalizing
Enriching
Organizing and storing
Summary
5
Data Exploration and Visualization
Data Exploration and Visualization
Sampling data
Performing ad hoc analysis
Finding a relationship between data elements
Visualizing data
Summary
6
Applying Statistics and Hypothesis Testing
Applying Statistics and Hypothesis Testing
Basics of statistics
Vector level statistics
Random data generation
Hypothesis testing
Summary
7
Section 2: Advanced Data Analysis and Machine Learning
Section 2: Advanced Data Analysis and Machine Learning
8
Introduction to Spark for Distributed Data Analysis
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
9
Traditional Machine Learning for Data Analysis
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
10
Section 3: Real-Time Data Analysis and Scalability
Section 3: Real-Time Data Analysis and Scalability
11
Near Real-Time Data Analysis Using Streaming
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
12
Working with Data at Scale
Working with Data at Scale
Working with data at scale
Cost considerations
Reliability considerations
Summary
13
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Traditional Machine Learning for Data Analysis

This chapter provides an overview of machine learning (ML) techniques for doing data analysis. In the previous chapters, we have explored some of the techniques that can be used by human beings to analyze and understand data. In this chapter, we look at how ML techniques could be used for similar purposes.

At the heart of ML is a number of algorithms that have proven to work for solving specific categories of problems with a high degree of effectiveness. This chapter covers the following popular ML methods:

  • Decision trees
  • Random forests
  • Ridge and lasso regression
  • k-means cluster analysis

It also covers the role of natural language processing (NLP) in effectively analyzing certain types of data problems. The discussion in this chapter is limited to traditional machine learning methods. It does not cover newer methods such as deep...

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