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MPA in Data Science for Public Policy

Programme Code: TMPPDS

Department: School of Public Policy

For students starting this programme of study in 2023/24

Guidelines for interpreting programme regulations

Classification scheme for the award of a taught master's degree (eight units)
Exam sub-board local rules

The programme is taught over two academic years (21 months).

To be awarded the degree, students must complete courses to the value of 8.0 units in total over two years with 4.0 units in each year of study. Some courses have a unit value of 1.0 and some have a unit value of 0.5 indicated as (H).

No interim award is available: students completing courses with a total value of less than 8.0 units receive no award, regardless of performance in the courses that have been successfully completed.

Paper

Course number, title (unit value)

Before Year 1

Introductory course

PP407 Pre-Sessional Coding and Mathematics Bootcamp (0.0)

Year 1

Paper 1

PP440 Micro and Macro Economics (for Public Policy) (1.0)

 

Or

 

Upon satisfactorily demonstrating prior knowledge of Micro and Macro Economics, students may be exempted from PP440 and will be free to take an additional unit of option course subject to the approval of the Programme Director.

Paper 2

PP478 Political Science for Public Policy (1.0)

Paper 3

PP455 Quantitative Approaches and Policy Analysis (1.0)

Paper 4

PP422 Data Science for Public Policy (1.0) #

Year 2

Paper 5

PP4B5 Capstone Project: MPA - Data Science for Public Policy (1.0)  (not available 2023/24)

Paper 6

PP415 Technology, Data Science and Policy (0.5) #

Paper 7

1.0 or 1.5 unit(s) from Public Policy:

 

HP434 Methods and Data for Health Systems Performance Assessment (0.5)

 

MA402 Mathematical Game Theory (0.5) # *

 

PP406 Philosophy for Public Policy (0.5)

 

PP410 Public Economics for Public Policy (0.5) #

 

PP411A Political Entrepreneurship (0.5)

 

PP411W Political Economy Applications for Public Policy (0.5) #

 

PP412 Cold War II? Public Policy Implications of US-China Relations in the 2020s (0.5)

 

PP413 Growth Diagnostics in Development: Theory and Practice (0.5) #

 

PP414 Policy-Making: Process, Challenges and Outcomes (0.5)

 

PP414W Policy-Making: Process, Challenges and Outcomes (0.5)  (not available 2023/24)

 

PP416 Beyond the policy cycle: how theory explains practice (0.5)  (not available 2023/24)

 

PP417A The Practice of Effective Climate Policy (0.5)  (not available 2023/24)

 

PP417W The Practice of Effective Climate Policy (0.5)

 

PP418 Globalisation and Economic Policy (0.5) #

 

PP419 Advanced Empirical Methods for Policy Analysis (0.5) #

 

PP423 Anticipatory Policymaking (0.5)

 

PP424 Happiness and Public Policy (0.5)

 

PP425 Strategic Policymaking: Economic Analysis, Narrative Development, Political Feasibility, and Implementation (0.5) #

 

PP426 Public Policy for Blockchains and Digital Assets (0.5) #

 

PP448 International Political Economy and Development (0.5)

 

PP449 Comparative Political Economy and Development (0.5)

 

PP450 Public Organisations: Theory and Practice (0.5) #

 

PP452 Applying Behavioural Economics for Social Impact: Design, Delivery, Evaluation and Policy (0.5) #

 

PP454 Development Economics (1.0) #

 

PP4E5 Innovations in the governance of public services delivery (0.5)

 

PP4G3 Designing and Managing Change in the Public Sector (0.5)

 

PP4J2 New Institutions of Public Policy: Strategic Philanthropy, Impact Investment and Social Enterprise (0.5)

 

PP4J4 Designing and Implementing Evidence-Informed Policies and Programmes (0.5)  (not available 2023/24)

 

PP4J5 Fiscal Governance and Budgeting (0.5)

 

PP4X6 Welfare Analysis and Measurement (1.0)

Paper 8

1.0 or 1.5 unit(s) from Data Sciences: A

 

MA407 Algorithms and Computation (0.5) # *

 

MA423 Fundamentals of Operations Research (0.5) # *

 

MY459 Quantitative Text Analysis (0.5) # B

 

MY461 Social Network Analysis (0.5)

 

MY474 Applied Machine Learning for Social Science (0.5) #

 

ST416 Multilevel Modelling (0.5) # *

 

ST442 Longitudinal Data Analysis (0.5) # *

 

ST443 Machine Learning and Data Mining (0.5) # *

 

ST446 Distributed Computing for Big Data (0.5) # *

 

ST449 Artificial Intelligence (0.5) *

 

ST454 Bayesian Data Analysis (0.5) # *

 

ST455 Reinforcement Learning (0.5) # *

 

ST456 Deep Learning (0.5) # *

 

MY472 Data for Data Scientists (0.5) 1 or

 

ST445 Managing and Visualising Data (0.5) # 2 *

Prerequisite Requirements and Mutually Exclusive Options

* means available with permission

# means there may be prerequisites for this course. Please view the course guide for more information.

1 : MY472 can not be taken with ST445

2 : ST445 can not be taken with MY472

Footnotes

A : MY459, MY461 and ST454 require knowledge of R

B : MY459: if instructor accepts a year of Econometrics and Data Science for Public Policy as pre-requisites.

SUPPLEMENTARY CRITERIA FOR PROGRESSION FROM THE FIRST TO THE SECOND YEAR OF THE MPA in Data Science for Public Policy

  • Students who attain at least a Pass grade in each of the first-year courses will be eligible to proceed into the second-year of the MPA in Data Science for Public Policy.
  • A student on the MPA in Data Science for Public Policy programme who has attained a Fail grade in courses to the value of 1.0 unit and at least a Pass grade in the remaining courses to the value of 3.0 units will be eligible to proceed into the second year.
  • A student who receives a Bad Fail in any course or who otherwise fails to meet the above criteria for progression will not be able to progress to the second year of the MPA in Data Science for Public Policy programme and will be entitled to repeat the failed courses as follows:

A student shall normally be entitled to repeat any failed courses only (on one occasion) and at the next normal opportunity, in accordance with paragraph 33 of the General Academic Regulations. The Repeat Teaching Panel may consider an application for repeat tuition in any failed courses from a student. Results obtained following a repeated attempt at assessment shall bear their normal value.

A student who has completed year one and is unable to complete year two of the MPA in Data Science for Public Policy programme will not receive an interim award.

Note for prospective students:
For changes to graduate course and programme information for the next academic session, please see the graduate summary page for prospective students. Changes to course and programme information for future academic sessions can be found on the graduate summary page for future students.