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
Table of Contents (14 chapters)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Hypothesis testing

Hypothesis testing is a statistical tool that is used for the following purposes:

  • Determining whether a result or model is statistically significant or not
  • Ensuring that a result or model did not occur by chance

A statistical hypothesis is used to establish a relationship between data using a sample set of observations. We can call this relationship a result or a model. The goal of hypothesis testing is to eliminate cases where a result occurs by chance. A null hypothesis, on the other hand, establishes that the relationship is not statistically significant.

We typically start with a sample set of observations that consists of values associated with more than one variable. In the Basics of statistics section, we looked at properties of a single variable in isolation, except for Pearson's correlation methodology, where we measured the linear relationship...