This module is designed to provide students with an introduction to the statistical principles used in data analytics and their application using a suitable statistical package. The module begins by considering how graphical summaries and numerical summaries, such as mean, median, standard deviation and correlation, can be used to describe and understand data. The issue of data handling is then considered. The basic concepts of inferential statistics are discussed and the use of methods for understanding the statistical importance of differences in means and proportions are described.
Syllabus: An Introduction to R – data import, data manipulation; introduction to data handling; basic graphical methods and numerical summaries; writing simple reports of a data analysis; basic concepts of statistics (populations and sampling); confidence intervals for means and proportions; testing for differences in means and proportions; p-values.
30 contact hours comprising a series of workshops
120 hours of private study
Total number of study hours: 150
100% coursework
Zuur, A. (2009) A Beginner's Guide to R, New York: Springer.
Verzani, J. (2014) Using R for Introductory Statistics, Second Edition, Chapman & Hall / CRC.
Mann, P. (2017) Introductory Statistics, 9th Edition, Wiley.
Weiss, N. A. (2014) Introductory Statistics, 9th Edition, Boston: Pearson Education.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1 demonstrate knowledge and critical understanding of the underlying concepts and principles of data analytic techniques;
2 demonstrate the capability to use a range of established techniques and a reasonable level of skill in the use of basic graphical and numerical summaries of data, confidence intervals and testing for means and proportions;
3 select and deploy the concepts and principles in the use of data analytics;
4 make appropriate use of a statistical package, including basic graphical and numerical summaries of data, and testing for means and proportions.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 make effective use of IT facilities for solving problems;
2 communicate straightforward arguments and conclusions reasonably accurately and clearly;
3 manage their own learning and development;
4 solve problems relating to quantitative and qualitative information.
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