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  • Book Overview & Buying Mastering Clojure Data Analysis
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Mastering Clojure Data Analysis

Mastering Clojure Data Analysis

By : Eric Richard Rochester
4.2 (5)
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Mastering Clojure Data Analysis

Mastering Clojure Data Analysis

4.2 (5)
By: Eric Richard Rochester
Buy this Book

Overview of this book

This book consists of a practical, example-oriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently. This book is great for those who have experience with Clojure and need to use it to perform data analysis. This book will also be hugely beneficial for readers with basic experience in data analysis and statistics.
Table of Contents (17 chapters)
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Mastering Clojure Data Analysis
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Mastering Clojure Data Analysis
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Network Analysis – The Six Degrees of Kevin Bacon
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Network Analysis – The Six Degrees of Kevin Bacon
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Analyzing social networks
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Getting the data
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Understanding graphs
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Implementing the graphs
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Measuring social network graphs
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Visualizing the graph
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Summary
2
GIS Analysis – Mapping Climate Change
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GIS Analysis – Mapping Climate Change
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Understanding GIS
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Mapping the climate change
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Working with map projections
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Working with ArcGIS
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Summary
3
Topic Modeling – Changing Concerns in the State of the Union Addresses
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Topic Modeling – Changing Concerns in the State of the Union Addresses
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Understanding data in the State of Union addresses
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Understanding topic modeling
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Preparing for visualizations
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Setting up the project
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Getting the data
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Summary
4
Classifying UFO Sightings
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Classifying UFO Sightings
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Getting the data
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Extracting the data
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Dealing with messy data
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Visualizing UFO data
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Description
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Topic modeling descriptions
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Hoaxes
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Summary
5
Benford's Law – Detecting Natural Progressions of Numbers
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Benford's Law – Detecting Natural Progressions of Numbers
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Learning about Benford's Law
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Failing Benford's Law
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Case studies
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Summary
6
Sentiment Analysis – Categorizing Hotel Reviews
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Sentiment Analysis – Categorizing Hotel Reviews
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Understanding sentiment analysis
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Getting hotel review data
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Exploring the data
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Preparing the data
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Cross-validating the results
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Calculating error rates
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Using the Weka machine learning library
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Running the experiment
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Examining the results
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Improving the results
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Summary
7
Null Hypothesis Tests – Analyzing Crime Data
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Null Hypothesis Tests – Analyzing Crime Data
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Introducing confirmatory data analysis
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Understanding null hypothesis testing
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Understanding burglary rates
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Exploring the data
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Conducting the experiment
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Interpreting the results
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Summary
8
A/B Testing – Statistical Experiments for the Web
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A/B Testing – Statistical Experiments for the Web
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Defining A/B testing
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Conducting an A/B test
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Summary
9
Analyzing Social Data Participation
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Analyzing Social Data Participation
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Setting up the project
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Summary
10
Modeling Stock Data
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Modeling Stock Data
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Learning about financial data analysis
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Setting up the basics
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Getting prepared with data
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Analyzing the text
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Inspecting the stock prices
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Merging text and stock features
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Analyzing both text and stock features together with neural nets
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Predicting the future
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Taking it with a grain of salt
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Summary
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Index
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Index

Understanding null hypothesis testing

One common way of structuring and processing these tests is to use null hypothesis testing. This represents a frequentist approach to statistical inference. This draws inferences based upon the frequencies or proportions in the data, paying attention to confidence intervals and error rates. Another approach is Bayesian inference, which focuses on degrees of belief, but we won't go into that in this chapter.

Frequentist inference has been very successful. Its use is assumed in many fields, such as the social sciences and biology. Its techniques are widely implemented in many libraries and software packages, and it's relatively easy to start using it. It's the approach we'll use in this chapter.

Understanding the process

To use the null hypothesis process, we should understand what we'll be doing at each step of the way. The following is the basic process that we'll work through in this chapter:

  1. Formulate an initial hypothesis.

  2. State the null (H0) and alternative...

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Tech Concepts
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Programming languages
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Mastering Clojure Data Analysis
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