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

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.

Modules

Year 1: Postgraduate Certificate: The course is divided into 4 modules. Two take place in the first semester and two take place in the second.

Year 2: Postgraduate Diploma: The course is divided into 6 modules, with 3 in each semester. Many of them build on what has been done in Year 1. The technical level, in terms of the mathematics, is also higher as we explore more sophisticated statistical methods.

Year 3: Master of Science: The course in Year 3 consists of a single module, the preparation of a research dissertation. You work remotely with faculty in the School on a research topic that is submitted as a dissertation at the end of the academic year.

Modules for Year 1 (Postgraduate Certificate)

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

 


 

STP80080: Foundations of Data Science 1

Hilary Term (Semester 2). Online through 

BLACKBOARD

Lecturer: Prof. Simon Wilson

  • Machine learning and how it differs from statistics; non-statistical ML methods (such as case-based reasoning), regression with neural networks; classification (support vector machines, kNN);
  • Evaluating ML methods: cross validation, ROC;  Installing and running Python. Its user interface and basic operations. 
  • Introduction to databases and how data is managed, including an introduction to SQL.
  • Industry case study.

MODULE DESCRIPTION ST80080

BLACKBOARD

Modules for Year 2 (Postgraduate Diploma)

Michaelmas Term (Semester 1). Online through

Blackboard

Lecturer: TBD

Topics covered:

  • Understanding the theory behind simple and multiple linear regression models;
  • Diagnostic issues with regression modules;
  • The use and application of generalised linear models;
  • Implemention with statistical software.

MODULE DESCRIPTION STP80010 

BLACKBOARD

Blackboard

Lecturer: Prof. James Ng

Topics covered:

  • The need for experiments: experimental and observational studies, control;
  • Basic design principles: control, blocking, randomisation, replication, factorial structures;
  • Standard designs;
  • Analysis of experimental data;

MODULE DESCRIPTION ST8004 

BLACKBOARD

Michaelmas Term (Semester 1). Online through 

BLACKBOARD

Lecturer: Prof. Simon Wilson

Topics covered:

  • Introduction to distributed computing;
  • Security and privacy in computer systems and networks;
  • Maching learning;
  • Industry case study.

MODULE DESCRIPTION STP80090 

BLACKBOARD

 

Hilary Term (Semester 2). Online through 

BLACKBOARD

Lecturer: TBD

  • Linear mixed models, random and fixed effects;
  • Repeated measures and longitudinal data modelling;
  • Model criticism and diagnostics;
  • Implementation in statistical software.

MODULE DESCRIPTION ST80110

BLACKBOARD

 

Hilary Term (Semester 2). Online through 

BLACKBOARD

Lecturer: TBD

  • Exponential smoothing and Holt Winters;
  • ARIMA modelling;
  • Diagnostics and using transformations;
  • Implementation in statistical software.

MODULE DESCRIPTION ST80120

BLACKBOARD

BLACKBOARD

Lecturer: TBD

  • Theory of multivariate theory;
  • Dimension reduction methods: principal components, factor analysis, multidimensional scaling;
  • Classification methods: discriminant analysis, logistic regression, nearest neighbour;
  • Decision trees and random forests;
  • Clustering;
  • Implementation in statistical software.

MODULE DESCRIPTION ST80130

BLACKBOARD