MSc Data Science (Social Analytics) / Course details

Year of entry: 2023

Course description

The range of pathways reflects the interdisciplinary nature of the course and we welcome applications from students with backgrounds in a range of disciplines, including:

  • Business and Management;
  • Health Science;
  • Social Sciences;
  • Geography;
  • Planning;
  • Computer Science;
  • Mathematics.   

We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, you will develop:

  • computational skills;
  • data analytical skills;
  • data stewardship skills and knowledge;
  • project design skills.


This innovative MSc in Data Science course is an opportunity for graduates from a broad range of disciplines to develop data science skills. Our goal is to help you develop into an agile, skilled data scientist, adept at working in variety of settings and able to meet the challenges and reap the rewards of interdisciplinary teamwork.

Teaching and learning

Teaching and Learning use multiple modes, including:

  • Lectures
  • Practical exercise
  • Individual and teamwork
  • Student presentations

Course unit details

Through a set of core units, you will develop a set of key data science skills.

The core units are:

  • Machine Learning and Statistics (both semesters);
  • Understanding Databases;
  • Understanding Data and their Environment;
  • Applications in Data Science.

Course unit list

The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

TitleCodeCredit ratingMandatory/optional
Statistics and Machine Learning 1: Statistical Foundations DATA70121 15 Mandatory
Statistics & Machine Learning 2: AI, Complex Data, Computationally Intensive Statistics DATA70132 15 Mandatory
Understanding Databases DATA70141 15 Mandatory
Applying Data Science DATA70202 15 Mandatory
Understanding Data and their Environment DATA71011 15 Mandatory
Dissertation DATA72000 60 Mandatory
Programming in Python for Business Analytics BMAN73701 15 Optional
Digital Planning - Spatial and Policy Analysis PLAN60761 15 Optional
Survey Research Methods SOST60421 15 Optional
Complex Survey Designs and Analysis SOST70032 15 Optional
Demographic Forecasting SOST70102 15 Optional
Quantitative Evaluation of Policies, Interventions and Experiments. SOST70172 15 Optional
Statistical Models for Social Networks SOST71032 15 Optional
Displaying 10 of 13 course units

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: