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Introduction to Data Science
Version 3
Jeffrey Stanton, Robert W. De Graaf
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Front Matter
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Note on This PreTeXt Edition
1
Data Science: Many Skills
1.1
Data in Everyday Life
1.2
The Four A’s of Data
1.3
Skills for Data Scientists
2
About Data
2.1
Bits and Bytes
2.2
Combining Bytes into Larger Structures
2.3
Chapter Challenge
3
Identifying Data Problems
3.1
Three Strategies for Identifying Data Problems
3.2
Strategy 1: Ask About Stories
3.3
Strategy 2: Look for Exception Cases
3.4
Strategy 3: Find Out About Risk and Uncertainty
4
Getting Started with R
4.1
Why Use R?
4.2
Installing and Running R
4.3
Working with Vectors in R
4.4
Chapter Challenge
5
Follow the Data
5.1
Following the Data in Healthcare
5.2
Data Modeling and Entity-Relationship Diagrams
6
Rows and Columns
6.1
Rows, Columns, and the Family Data Set
6.2
Creating a Data Frame in R
6.3
Key Points from This Chapter
6.4
Chapter Challenge
7
Beer, Farms, and Peas
7.1
Four Founding Statisticians
7.2
Sample vs. Population
7.3
Descriptive Statistics
7.4
Working with the U.S. State Population Data
7.5
Chapter Challenge
8
Sample in a Jar
8.1
The Gumball Jar: Introducing Sampling
8.2
Sampling the U.S. State Data in R
8.3
Using the Sampling Distribution for Inference
8.4
Chapter Challenge
9
Big Data? Big Deal!
9.1
Big Data in Context
9.2
Cautions About Big Data
9.3
The Tools of Data Science
9.4
Chapter Challenge
10
Onward with R-Studio
10.1
Introduction to R-Studio
10.2
Writing Your First Function
10.3
Using R Packages
10.4
Chapter Challenge
11
Tweet, Tweet!
11.1
Extending R
11.2
A Token of Your Esteem: Using OAuth
11.3
Working with twitteR
11.4
Getting New SSL Tokens on Windows
11.5
Using Your OAuth Tokens
11.6
Ready, Set, Go!
11.7
Tweet Arrival Times and the Poisson Distribution
11.8
Chapter Challenge
12
Popularity Contest
12.1
Which Topic Is More Popular?
12.2
Simulating Poisson Arrivals
12.3
Comparing Arrival Probabilities
12.4
Confidence Intervals for Poisson Rates
12.5
Comparing Real Twitter Data
12.6
Chapter Challenge
13
String Theory
13.1
Text as Data
13.2
Basic String Operations
13.3
Contingency Tables for String Flags
13.4
Chapter Challenges
14
Word Perfect
14.1
Word Clouds and the Corpus
14.2
The Term-Document Matrix
14.3
Generating the Word Cloud
14.4
Chapter Challenge
15
Storage Wars
15.1
Evolution of Technology
15.2
Data Formats and Importing Data
15.3
Connecting to Databases
15.4
Large Data and Hadoop
15.5
Chapter Challenge
16
Map MashUp
16.1
Shapefiles and Mapping Packages
16.2
Geocoding and Combining Data
16.3
Chapter Challenge(s)
17
Line Up, Please
17.1
Finding Relationships Between Sets of Data
17.2
Football or Rugby? The Australian Rules Football Example
17.3
Building a Linear Model with
lm()
17.4
Adding Another Independent Variable
17.5
Conclusion
17.6
Chapter Challenge
18
Hi Ho, Hi Ho — Data Mining We Go
18.1
What Is Data Mining?
18.2
Association Rules Mining
18.3
Running the Apriori Algorithm
18.4
Chapter Challenge
18.5
Data Mining with Rattle
19
What’s Your Vector, Victor?
19.1
Supervised vs. Unsupervised Learning
19.2
How Support Vector Machines Work
19.3
Classifying Spam Email with kernlab
19.4
Training and Evaluating the SVM
19.5
Chapter Challenge
Back Matter
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©2012–2013 Jeffrey Stanton
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit
CreativeCommons.org
.
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