Mastering Clojure Data Analysis

If you’d like to apply your Clojure skills to performing data analysis, this is the book for you. The example based approach aids fast learning and covers basic to advanced topics. Get deeper into your data.

Mastering Clojure Data Analysis

Eric Rochester

If you’d like to apply your Clojure skills to performing data analysis, this is the book for you. The example based approach aids fast learning and covers basic to advanced topics. Get deeper into your data.
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Book Details

ISBN 139781783284139
Paperback340 pages

Book Description

Clojure is a Lisp dialect built on top of the Java Virtual Machine. As data increasingly invades more and more parts of our lives, we continually need more tools to deal with it effectively. Data can be organized effectively using Clojure data tools.

Mastering Clojure Data Analysis teaches you how to analyze and visualize complex datasets. With this book, you'll learn how to perform data analysis using established scientific methods with the modern, powerful Clojure programming language with the help of exciting examples drawn from real-world data. This will help you get to grips with advanced topics such as network analysis, the characteristics of social networks, applying topic modeling to get a handle on unstructured textual data, and GIS analysis to apply geospatial techniques to your data analysis problems.

With this guide, you'll learn how to leverage the power and flexibility of Clojure to dig into your data and access the insights it hides.

Table of Contents

Chapter 5: Benford's Law – Detecting Natural Progressions of Numbers
Chapter 8: A/B Testing – Statistical Experiments for the Web
Chapter 9: Analyzing Social Data Participation

What You Will Learn

  • Use geospatial data to learn about geographical patterns in data
  • Use sentiment analysis to determine people's opinions from online reviews
  • Frame and implement statistical experiments
  • Use A/B testing to determine the best UI to keep users engaged
  • Work with time series data
  • Learn how to use parallelization and concurrency to work with large datasets
  • Use topic modeling to find the subjects discussed in a group of documents
  • Use network analysis to learn about online social networks

Authors

Table of Contents

Chapter 5: Benford's Law – Detecting Natural Progressions of Numbers
Chapter 8: A/B Testing – Statistical Experiments for the Web
Chapter 9: Analyzing Social Data Participation

Book Details

ISBN 139781783284139
Paperback340 pages
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