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.

Author Biography

Dr Graham Williams is lead Data Scientist at the Australian Taxation Office, and was previously Principal Computer Scientist for Data Mining with CSIRO Australia. He is a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences at the Shenzhen Institutes of Advanced Technologies. He is also Adjunct Professor, Data Mining, Fraud Prevention, Security, University of Canberra, and Australian National University. Graham has been involved in data mining since the 1980s as a researcher and practitioner. He has lead projects with clients including the Health Insurance Commission, the Australian Taxation Office, the Commonwealth Bank, NRMA Insurance Limited, the Commonwealth Department of Health and Ageing, Queensland Health, and the Australian Customs Service. He has developed software and hardware environments for data mining, and implemented web services for the delivery of data mining. His research developments include Multiple (or Ensemble) Decision Tree Induction (1989), HotSpots for identifying target areas in very large data collections (1992), WebDM for the delivery of data mining services over the web using XML (1995), and Rattle (2005), a simple to use Graphical User Interface designed to make data mining accessible for data analysts. His popular text book on Data Mining with Rattle and R was published by Springer in 2011. His OnePageR website is an increasingly popular resource for data miners using R.