Data analysis and statistics with R

The details
Mathematical Sciences
Colchester Campus
Postgraduate: Level 7
Monday 18 January 2021
Friday 26 March 2021
10 September 2020


Requisites for this module



Key module for

MSC G305JS Applied Data Science

Module description

The module will introduce concepts from data analysis and statistics and show how they can be applied effectively via the R language. It will cover a wide introduction to statistics and provide practical experience of real-world examples of how statistics is used to gain insights.

Throughout these examples, and many more, we will teach programming techniques that will enable students to apply statistical approaches to real-world applications.

This module assumes no previous exposure to statistics.

Module aims

The purpose of this module is to introduce:

Data analysis.
The use of R for data analysis and statistics.

Module learning outcomes

A. A systematic, extensive and comparative knowledge and understanding of the use of R for carrying out statistical analysis
B. A systematic, extensive and comparative knowledge and understanding of data analysis methods
C. A systematic, extensive and comparative knowledge and understanding of statistical methods

Module information

Basic ideas of probability and statistical distributions
Random variables, means, covariance and variance
Variance of a sample mean and confidence intervals for means, variances and differences between means
Conditional probability and independence
Probability distribution theory
Standard distributions and their use in modelling: including Bernoulli, Binomial, Poisson
Estimation and Maximum Likelihood estimators
Hypothesis tests concerning means and variances
Null and alternative hypotheses
Type I and type II errors
Test statistic and critical region
Probability value and level of significance
Using tables of the t, F and chi-squared distributions
Introduction to linear regression
The least square estimates of the intercept and the slope of a simple linear regression
Confidence intervals for the slope parameter and prediction intervals for response
Coefficient of determination and the sample correlation coefficient

Learning and teaching methods

This module has 35 contact hours that will be structured as follows: Lectures: 20 hours Classes: 10 hours Computer labs: 5 hours


  • Upton, Graham J. G; Cook, Ian. (2001) Introducing statistics, Oxford: Oxford University Press.
  • Rowntree, Derek. (2018) Statistics without tears: a primer for non-mathematicians, London: Penguin Books.
  • Wheelan, Charles J. (c2013) Naked statistics: stripping the dread from the data, New York: W.W. Norton.
  • Spiegelhalter, D. J. (2019) The art of statistics: learning from data, [London] UK: Pelican, an imprint of Penguin Books.
  • Wickham, Hadley; Grolemund, Garrett. (2016-12-12) R for Data Science: O'Reilly Media.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Final Project & Presentation  30/04/2021   

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Andrew Harrison, email:
Dr Andrew Harrison, Dr Osama Mahmoud & Dr Mario Gutierrez-Roig
Dr Andrew Harrison (, Dr Osama Mahmoud (, Dr Mario Gutierrez-Roig (



External examiner

Prof Fionn Murtagh
University of Huddersfield
Professor of Data Science
Available via Moodle
Of 2386 hours, 0 (0%) hours available to students:
2386 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information
Mathematical Sciences

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