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

Finding a relationship between data elements

Once we have a decent understanding of the data and some of its main properties, the next step is to find a concrete relationship between data elements. We can use some of the well-established statistical techniques to understand the distribution of data.

Let's continue with our Spark example from the previous section by comparing Total Population to Total Households. We can expect the two numbers to be strongly correlated:

println("Covariance: " + df.stat.cov("Total Population", "Total Households"))
println("Correlation: " + df.stat.corr("Total Population", "Total Households"))

The output from this would be something like this:

Covariance: 1.2338126298368526E8
Correlation: 0.9090567549637986

As expected, we see the correlation coefficient value closer to 1, indicating a...