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Mastering Clojure Data Analysis

By : Eric Richard Rochester
Book Image

Mastering Clojure Data Analysis

By: Eric Richard Rochester

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Mastering Clojure Data Analysis
Eric Richard Rochester
May, 2014 | 340 pages
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Table of Contents (17 chapters)
Mastering Clojure Data Analysis
Mastering Clojure Data Analysis
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Network Analysis – The Six Degrees of Kevin Bacon
Network Analysis – The Six Degrees of Kevin Bacon
Analyzing social networks
Getting the data
Understanding graphs
Implementing the graphs
Measuring social network graphs
Visualizing the graph
Summary
2
GIS Analysis – Mapping Climate Change
GIS Analysis – Mapping Climate Change
Understanding GIS
Mapping the climate change
Working with map projections
Working with ArcGIS
Summary
3
Topic Modeling – Changing Concerns in the State of the Union Addresses
Topic Modeling – Changing Concerns in the State of the Union Addresses
Understanding data in the State of Union addresses
Understanding topic modeling
Preparing for visualizations
Setting up the project
Getting the data
Summary
4
Classifying UFO Sightings
Classifying UFO Sightings
Getting the data
Extracting the data
Dealing with messy data
Visualizing UFO data
Description
Topic modeling descriptions
Hoaxes
Summary
5
Benford's Law – Detecting Natural Progressions of Numbers
Benford's Law – Detecting Natural Progressions of Numbers
Learning about Benford's Law
Failing Benford's Law
Case studies
Summary
6
Sentiment Analysis – Categorizing Hotel Reviews
Sentiment Analysis – Categorizing Hotel Reviews
Understanding sentiment analysis
Getting hotel review data
Exploring the data
Preparing the data
Cross-validating the results
Calculating error rates
Using the Weka machine learning library
Running the experiment
Examining the results
Improving the results
Summary
7
Null Hypothesis Tests – Analyzing Crime Data
Null Hypothesis Tests – Analyzing Crime Data
Introducing confirmatory data analysis
Understanding null hypothesis testing
Understanding burglary rates
Exploring the data
Conducting the experiment
Interpreting the results
Summary
8
A/B Testing – Statistical Experiments for the Web
A/B Testing – Statistical Experiments for the Web
Defining A/B testing
Conducting an A/B test
Summary
9
Analyzing Social Data Participation
Analyzing Social Data Participation
Setting up the project
Summary
10
Modeling Stock Data
Modeling Stock Data
Learning about financial data analysis
Setting up the basics
Getting prepared with data
Analyzing the text
Inspecting the stock prices
Merging text and stock features
Analyzing both text and stock features together with neural nets
Predicting the future
Taking it with a grain of salt
Summary
Index
Index

Preparing for visualizations

One of the basic tools of data analysis is visualization. Good, flexible visualizations make it easier to explore and understand the data, and this is useful at all stages of the data analysis process. At the beginning, visualizations make it easier to find errors and inconsistencies and to get to know your data and developing an intuition for it. It continues to drive insights throughout the process. In the end, visualizations make great supporting evidence and explanations in reports and presentations.

Visualizations will be an important part of this chapter and in understanding the results of topic modeling. To create and interact with the graphs, we're going to use some software that's recently become an important part of many data scientists' toolkits: the Web browser.

As we did in Chapter 1, Network Analysis – The Six Degrees of Kevin Bacon, we'll use D3 (http://d3js.org/) and ClojureScript (https://github.com/clojure/clojurescript/).

The graph of the word...

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