Introductory description

This module runs in Term 2.

This module is not available to students within the Dept of Statistics, who take ST117 instead. This module is available for external students who have taken the necessary prerequisites. This module will be useful for ST231 Statistical Modelling and other modules which use statistical data analysis such as ST340 Programming for Data Science and ST323 Multivariate Statistics.

Pre-requisites for non-Statistics Students: ST120 Introduction to Probability.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to R
Exploratory data analysis: methods of visualisation and summary statistics
Sampling from standard discrete and continuous distributions (Bernoulli, Geometric, Poisson, Gaussian, Gamma)
Generic methods for sampling from univariate distributions
The use of R to illustrate probabilistic notions such as conditioning, convolutions and the law of large numbers
Examples of modelling real data (but without formal statistical inference) and the use of visualisations to assess fit

Learning outcomes

By the end of the module, students should be able to:

• Gain familiarity with the R software package, making use of it for exploratory data analysis.
• Use R to simulate samples from a variety of probability distributions.
• Gain the ability to propose appropriate probabilistic models for simple data sets.

Indicative reading list

"R for Data Science" by H.Wickham; G.Grolemund 2016
"Hands-On Programming with R" by G.Grolemund 2014
"Statistical methods" by Rudolf J. Freund; William J. Wilson; Donna L. Mohr 2010.
"Problem solving: a statistician's guide" by Christopher Chatfield 2017.
"Introduction to the practice of statistics" David S. Moore; George P. McCabe; Bruce Craig 2012.
"Introductory statistics with R" Peter Dalgaard 2008.

View reading list on Talis Aspire

Subject specific skills

Select and apply appropriate mathematical and/or statistical techniques.
Create structured and coherent arguments communicating them in written form.
Communicate subject-specific information effectively and coherently.
Analyse problems, abstracting their essential information formulating them using appropriate mathematical language to facilitate their solution.
Select and apply appropriate statistical programming language (for example, R) for exploratory data analysis

Transferable skills

Critical thinking: extracting patterns from incomplete data and using them to form evidence-based conclusions.
Problem solving: use of logical reasoning to build arguments grounded in evidence and with explicit underlying assumptions.
Self-awareness: monitoring of your own learning and seeking feedback.
Communication: verbal discussion of ideas in seminars and among peers; written communication in assignments.
Information literacy: evaluation of data and uncertainty in a model-based way.
Digital literacy: use of computational tools to understand and visualise data, and to produce reports.
Professionalism: self-motivation, taking charge of your own learning, and prioritising effectively.