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Postgraduate Programmes in Statistics and Data Science  (Online) Course Structure

Course Philosophy

The emphasis is on statistical thinking rather than mathematical techniques, consequently statistical or mathematical theory are not discussed. The conceptual basis of the methods is emphasised; the aim is to develop an intuitive understanding of how the methods work. Underlying assumptions of the standard methods and what can be done when these assumptions are invalid are discussed. While it is likely that most participants will have some previous exposure to statistics as undergraduates, the course does not assume prior knowledge of statistical ideas and methods. However, because all participants are graduates, the coverage is conceptually more sophisticated than most undergraduate first level courses.

For Whom is the Course Intended?

The course is intended for graduates of disciplines, other than statistics, who want to develop and deepen their knowledge of statistical methods for solving problems involving data arising in business and industry, in public service agencies or in research agencies. Applications will be considered from degree level graduates in any discipline. While the mathematical level of the course is kept to a minimum, some background in mathematics is essential; Leaving Certificate mathematics is an acceptable standard for most modules.

Many people taking research degrees in other disciplines in Trinity College take the Postgraduate Certificate as a means of developing their research methods skills. This is encouraged by the College and in such cases the tuition fees for the Postgraduate Certificate are waived. Note, though, that students need to register separately for the Postgraduate Certificate - registration for the research degree is not sufficient.

Students taking taught postgraduate courses are NOT NORMALLY GIVEN PERMISSION to take the Postgraduate Certificate in parallel. Students who wish to do so need to apply to the Dean of Graduate Studies for permission. In doing so they should provide a letter/email indicating the support of their Course Director for their request. SUCH PERMISSION IS ONLY GRANTED EXCEPTIONALLY.

The Postgraduate Certificate is designed to be a challenging course for graduates of disciplines other than Statistics.  The great majority of participants will have studied some Statistics at undergraduate level, but this will often have been taught in a cookbook fashion by non-statisticians.  The course aims to develop and enhance the data analytic skills of non-statistical graduates by teaching in a unified and coherent way the inferential ideas and methods of Applied Statistics.  It is not designed for Statistics graduates.  Neither is it an entry point for postgraduate study in Statistical Science and it does not lead on to a Masters level degree in the discipline of Statistics.

How the Course is Delivered

From 2021 this course is delivered entirely online. There is no need to live close to Dublin or have access to Trinity College campus in order to study the course. Each week, new content is released through the College website that students work through. This content consists of reading, a video lecture, quizes and other homework, etc. Each week there is also a live tutorial with a demonstrator to go through the content and address any questions that you have. Assessment of the course is a mixture of take home exams, homework and projects.


The course is divided into 4 modules. Two take place in the first semester and two take place in the second.

ST8001: Introduction to statistical concepts and methods

Michaelmas Term (Semester 1). Online through Blackboard

Lecturer: Prof. Mimi Zhang

Topics covered:

  • Data summaries and graphs
  • Statistical models
  • Sampling distributions: confidence intervals and tests
  • Comparative experiments: t-tests, confidence intervals, design issues
  • Counted data: confidence intervals and tests for proportions, design issues
  • Cross-classified frequency data: chi-square tests

Module Description ST8001 Blackboard

ST8002: Implementing statistical methods in R

Michaelmas Term (Semester 1). Online through Blackboard

Lecturer: Prof. Mimi Zhang

Topics covered:

  • Installing and running R through the RStudio environment
  • Basics of the RSudio user interface
  • Data import
  • Data formatting and plotting
  • Implementing the statistical methods in ST8001 (estimation and tests)

Module Description ST8002 Blackboard

ST8003: Linear regression

Hilary Term (Semester 2). Online through Blackboard

Lecturer: Prof. John McDonagh

Topics covered:

  • Review of simple linear regression and its assumptions
  • Multiple linear regression modelling and its analysis, including
    • Confidence intervals and significance tests
    • Interpreting the model parameters
    • Analysis of variance, F-tests, r-squared
    • Indicator variables and interaction terms
    • Model validation through residuals and other diagnostics
    • Logistic regression

Module Description ST8003 Blackboard

ST8004: Introduction to experimental design

Hilary Term (Semester 2). Online through Blackboard

Lecturer: Prof. James Ng

Topics covered:

  • The need for experiments: experimental and observational studies, cause and effect, control
  • Basic design principles for experiments: Control, Blocking (pairing), Randomisation, Replication, Factorial structure
  • Standard designs: Randomised blocks, Two-level factors, Multi-level factors, Split units
  • Analysis of experimental data, Exploratory data analysis, Effect estimation and significance testing, Analysis of variance, Statistical models, fixed and random effects, Model validation, diagnostics

Module Description ST8004 Blackboard