Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Hands-On Data Analysis with Scala
  • Toc
  • feedback
Hands-On Data Analysis with Scala

Hands-On Data Analysis with Scala

By : Gupta
5 (3)
close
Hands-On Data Analysis with Scala

Hands-On Data Analysis with Scala

5 (3)
By: 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)
close
Free Chapter
1
Section 1: Scala and Data Analysis Life Cycle
7
Section 2: Advanced Data Analysis and Machine Learning
10
Section 3: Real-Time Data Analysis and Scalability

Summary

Statistics play an important role in the data analysis life cycle. This chapter provided an overview of basic statistics. We also learned how to extend basic statistical techniques and use them on data that is represented as vectors. In the vector bases stats, we got some insights into how weights could significantly alter statistical outcomes. We also learned various techniques for random data generation, and, finally, we took a high-level view of how to perform hypothesis testing.

In the next chapter, we will focus on Spark, a distributed data analysis and processing framework.

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete