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The essentials of data science : knowledge discovery using R / Graham J. Williams.
Author
Williams, Graham J.
[Browse]
Format
Book
Language
English
Published/Created
Boca Raton, FL ; New York, NY : CRC Press, an imprint of the Taylor & Francis Group, [2017]
©2017
Description
1 online resource (xviii, 322 pages)
Availability
Available Online
Taylor & Francis eBooks Complete
SCI-TECHnetBASE
Ebook Central Perpetual, DDA and Subscription Titles
Details
Subject(s)
Big data
[Browse]
Computational intelligence
[Browse]
R (Computer program language)
[Browse]
Series
Chapman & Hall/CRC the R series (CRC Press)
[More in this series]
Chapman & Hall/CRC the R series
Summary note
"The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years' experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R's capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book."--Provided by publisher
Bibliographic references
Includes bibliographical references and index.
Source of description
Print version record.
Contents
Chapter 1 Data Science
1.1 Exercises
Chapter 2 Introducing R
2.1 Tooling For R Programming
2.2 Packages and Libraries
2.3 Functions, Commands and Operators
2.4 Pipes
2.5 Getting Help
2.6 Exercises
Chapter 3 Data Wrangling
3.1 Data Ingestion
3.2 Data Review
3.3 Data Cleaning
3.4 Variable Roles
3.5 Feature Selection
3.6 Missing Data
3.7 Feature Creation
3.8 Preparing the Metadata
3.9 Preparing for Model Building
3.10 Save the Dataset
3.11 A Template for Data Preparation
3.12 Exercises
Chapter 4 Visualising Data
4.1 Preparing the Dataset
4.2 Scatter Plot
4.3 Bar Chart
4.4 Saving Plots to File
4.5 Adding Spice to the Bar Chart
4.6 Alternative Bar Charts
4.7 Box Plots
4.8 Exercises
Chapter 5 Case Study: Australian Ports
5.1 Data Ingestion
5.2 Bar Chart: Value/Weight of Sea Trade
5.3 Scatter Plot: Throughput versus Annual Growth
5.4 Combined Plots: Port Calls
5.5 Further Plots
5.6 Exercises
Chapter 6 Case Study: Web Analytics
6.1 Sourcing Data from CKAN
6.2 Browser Data
6.3 Entry Pages
6.4 Exercises
Chapter 7 A Pattern for Predictive Modelling
7.1 Loading the Dataset
7.2 Building a Decision Tree Model
7.3 Model Performance
7.4 Evaluating Model Generality
7.5 Model Tuning
7.6 Comparison of Performance Measures
7.7 Save the Model to File
7.8 A Template for Predictive Modelling
7.9 Exercises
Chapter 8 Ensemble of Predictive Models
8.1 Loading the Dataset
8.2 Random Forest
8.3 Extreme Gradient Boosting
8.4 Exercises
Chapter 9 Writing Functions in R
9.1 Model Evaluation
9.2 Creating a Function.
9.3 Function for ROC Curves
9.4 Exercises
Chapter 10 Literate Data Science
10.1 Basic LATEX Template
10.2 A Template for our Narrative
10.3 Including R Commands
10.4 Inline R Code
10.5 Formatting Tables Using Kable
10.6 Formatting Tables Using XTable
10.7 Including Figures
10.8 Add a Caption and Label
10.9 Knitr Options
10.10Exercises
Chapter 11 R with Style
11.1 Why We Should Care
11.2 Naming
11.3 Comments
11.4 Layout
11.5 Functions
11.6 Assignment
11.7 Miscellaneous
11.8 Exercises
Bibliography
Index.
Show 82 more Contents items
ISBN
9781498740012 ((electronic bk.))
1498740014 ((electronic bk.))
9781351647496 ((electronic bk.))
1351647490 ((electronic bk.))
9781315151458 ((electronic bk.))
1315151456 ((electronic bk.))
OCLC
999643978
Doi
10.1201/9781315151458
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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The Essentials of Data Science : Knowledge Discovery Using R / Graham J. Williams.
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The essentials of data science : knowledge discovery using R / Graham J. Williams.
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