The essentials of data science : knowledge discovery using R
- Responsibility
- Graham J. Williams.
- Publication
- Boca Raton, FL ; New York, NY : CRC Press, an imprint of the Taylor & Francis Group, 2017.
- Physical description
- xix, 322 pages ; 24 cm.
- Series
- Chapman & Hall/CRC the R series (CRC Press)
At the library
Science Library (Li and Ma)
Stacks
| Call number | Status |
|---|---|
| QA76.9 .D32 W56 2018 |
More options
Description
Creators/Contributors
- Author/Creator
- Williams, Graham J., author.
Contents/Summary
- Bibliography
- Includes bibliographical references and index.
- Contents
-
- Part I - An Overview for the Data Scientist. Data Science, Analytics, and Data Mining. From Rattle to R for the Data Scientist. Preparing Data. Building Models. Case Studies. R Basics. Part II - Data Foundations. Reading Data into R. Exploring and Summarising Data. Transforming Data. Presenting Data. Part III - Analytics. Descriptive Analytics. Predictive Analytics. Prescriptive Analytics. Text Analytics. Social Network Analytics. Part IV - Advanced Data Science in R. Dealing with Big Data. Parallel Processing for High Performance Analytics. Ensembles for Big Data.
- (source: Nielsen Book Data)9781498740005 20180813
- Publisher's Summary
- 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.
(source: Nielsen Book Data)9781498740005 20180813
Subjects
Bibliographic information
- Publication date
- 2017
- Series
- Chapman & Hall/CRC the R series (CRC Press)
- ISBN
- 9781138088634 paperback alkaline paper
- 1138088633 paperback alkaline paper
- 9781498740005 hardback alkaline paper
- 1498740006 hardback alkaline paper
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