Applied Statistics and Datamining (PGDip/MSc) 2023 entry

The PGDip/MSc in Applied Statistics and Datamining is a commercially relevant programme of study providing students with the statistical data analysis skills needed for business, commerce and other applications.

Start date
September 2023
End date
June 2024 (PGDip) or September 2024 (MSc)
Duration
Ten months full-time (PGDip) or one-year full-time (MSc)
School
School of Mathematics and Statistics

Application deadline

Thursday 10 August 2023

Applicants should apply as early as possible to be eligible for certain scholarships and for international visa purposes.

“The highlight of studying here is meeting people from all over the world, making lifelong friends and exploring the picturesque town of St Andrews. Don't hesitate to take the opportunity of gaining academic training from knowledgeable and passionate lecturers while experiencing a unique student life.”
Anna
- Paphos, Cyprus

Entry requirements

The qualifications listed are indicative minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.

Application requirements

  • CV 
  • two original signed academic references 
  • academic transcripts and degree certificates  
  • personal statement (optional).

For more guidance, see supporting documents and references for postgraduate taught programmes.

English language proficiency

If English is not your first language, you may need to provide an English language test score to evidence your English language ability. See approved English language tests and scores for this course.

Course details

The PGDip/MSc in Applied Statistics and Datamining is a taught programme run by the School of Mathematics and Statistics. The course is aimed at those with a good degree containing quantitative elements who wish to gain statistical data analysis skills. 

Highlights 

  • Commercially relevant course. 
  • Course content is aligned to the requirements of the commercial analysis sector. 
  • Dissertation topics are generated in part by commercial partners. 
  • Teaching involves widely used commercial software packages (SAS, SPSS). 
  • The popular open-source tool R is also used.  

Modules

The modules in this programme have varying methods of delivery and assessment. For more details of each module, including weekly contact hours, teaching methods and assessment, please see the latest module catalogue which is for the 2022-2023  academic year; some elements may be subject to change for 2023 entry.

  • Advanced Data Analysis: covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice.
  • Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
  • Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.
  • Knowledge Discovery and Datamining: covers many of the methods found under the banner of "datamining", building from a theoretical perspective but ultimately teaching practical application.
  • Multivariate Analysis: introductory and advanced training in the applied analysis of multivariate data.
  • Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.

Students choose one optional module, which can be chosen from the School's modules at level 3000 or above.

Undergraduate-level modules

  • Bayesian Inference
  • Classical Statistical Inference
  • Computing in Mathematics
  • Computing in Statistics
  • Design of Experiments
  • Financial Mathematics
  • Markov Chains and Processes
  • Population Dynamics Models in Mathematical Biology
  • Population Genetics
  • Quantitative Risk Management 
  • Sampling Theory
  • Spatial Statistics
  • Time Series Analysis

Postgraduate-level modules

  • Advanced Bayesian Inference
  • Advanced Combinatorics
  • Estimating Animal Abundance and Biodiversity
  • Independent Study Module
  • Mathematical Oncology
  • Medical Statistics
  • Modelling Wildlife population dynamics
  • Spatial Models and Pattern Formation in Mathematical Biology 

Computer Science modules 

In addition, students may take modules from the School of Computer Science that are consistent with the degree. Representative examples of these modules are:

  • Data-Intensive Systems
  • Database Management Systems
  • Information Visualisation

Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students.

MSc students complete a dissertation during the final three months of the course to be submitted near the end of August. Dissertations are supervised by members of teaching staff who will advise on the choice of subject and provide guidance throughout the progress of the dissertation. Many topics are in collaboration with companies and other external bodies.

If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma. By choosing an exit award, you will finish your degree at the end of the second semester of study and receive a PGDip instead of an MSc

The modules listed here are indicative, and there is no guarantee they will run for 2023 entry. Take a look at the most up-to-date modules in the module catalogue.

Teaching

The programme consists of two semesters with taught components which include a mixture of short, intensive courses with a large proportion of continuous assessment and more traditional lecture courses with end-of-semester exams. 

For those on the MSc, the taught component will be followed by a dissertation project taking place during the last three months of the course. 

The School of Mathematics and Statistics is well equipped with personal computers and laptops, a parallel computer and an on-site library. 

Events

There are a number of different seminars held each week in the School of Mathematics and Statistics. These include: 

Pure Mathematics 

Statistics 

Fees

Home
£11,120

Overseas
£23,530

More information on tuition fees can be found on the postgraduate fees and funding page.

Funding and scholarships

The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances. Find out more about the scholarships and postgraduate loans available.

All taught postgraduate scholarships

After your degree

Careers

Graduates from this programme typically seek employment as analysts within a company, research body, government, or as statistical consultants. 

Recent graduates have found employment in: 

  • large consulting firms and major financial institutions including:
    • American Express
    • Aviva
    • Capital One
    • Goldman Sachs
    • Lloyds
    • PwC
    • RBS
    • Scottish and Southern Energy
    • Tesco Bank 
    • TSB
    • Vodafone
  • biomedical research, clinical trials and pharmaceuticals 
  • wildlife and conservation managers including the Wildlife Conservation Society (WCS). 

The Careers Centre offers one-to-one advice to all students as well as a programme of events to assist students in building their employability skills.


Further study

The MSc in Applied Statistics and Datamining prepares students for further postgraduate studies in statistical data research, and many graduates of the programme continue their education by enrolling in PhD programmes at St Andrews or elsewhere. 

The School of Mathematics and Statistics has active research groups in: 

  • Applied Mathematics (Vortex Dynamics Group, Solar and Magnetospheric Theory Group, Mathematical Biology Group) 
  • Pure Mathematics (Analysis Group, Algebra and Combinatorics Group) 
  • Mathematical Biology (Mathematical Oncology, Cell Migration and Tissue Growth) 
  • Statistics (Statistical Ecology, Statistical Medicine and Molecular Biology, and Statistical Methodology). 
Postgraduate research

What to do next

Online information events

Join us for one of our information events where you can find out about different levels of study and specific courses we run. There are also sessions available for parents and college counsellors.

Postgraduate virtual days

We encourage all students who are thinking of applying to the University to attend one of our online visiting days.

Contact us

Phone
+44 (0)1334 46 2344
Email
maths-pgstats@st-andrews.ac.uk
Address
School of Mathematics and Statistics
Mathematical Institute
North Haugh
St Andrews
KY16 9SS

School of Mathematics and Statistics website