MPA in Data Science for Public Policy

Page contents > Prerequisite Requirements and Mutually Exclusive Options | Footnotes

Programme Code: TMPPDS

Department: School of Public Policy

For students starting this programme of study in 2024/25

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.

Before Year 1
Paper	Course number, title (unit value)
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)
	Or
	PP456 Quantitative Approaches and Policy Analysis (0.5) ¹
	and an additional 0.5 unit option course - for students who demonstrate sufficient prior knowledge of Econometrics to be exempt from the first half of PP455
	or
	PP419 Advanced Empirical Methods for Policy Analysis (0.5) ^#
	and an additional 0.5 unit option course - for students who demonstrate sufficient prior knowledge of Econometrics to be exempt from PP455 in its entirety
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)
Paper 6	PP415 Technology, Data Science and Policy (0.5) ^#
Paper 7	1.0 or 1.5 unit(s) from Public Policy:
	PP406 Philosophy for Public Policy (0.5)
	PP410 Public Economics for Public Policy (0.5) ^#
	PP411W Political Economy Applications for Public Policy (0.5) ^#
	PP413 Growth Diagnostics in Development: Theory and Practice (0.5) ^#
	PP414 Policy-Making: Process, Challenges and Outcomes (0.5)
	PP417A The Practice of Effective Climate Policy (0.5)
	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)
	PP426 Public Policy for Blockchains and Digital Assets (0.5) ^#
	PP431 Reimagining Capitalism (0.5)
	PP432 International Organisations, Policymaking and Diplomacy in a contested world (0.5)
	PP433 Topics in Model Based Quantitative Analysis for Public Policy (0.5) ^#
	PP434 Automated Data Visualisation for Policymaking (0.5)
	PP435 Trade Policy and Development (0.5) ^#
	PP448 International Political Economy and Development (0.5)
	PP449 Comparative Political Economy and Development (0.5)
	PP452 Applying Behavioural Economics for Social Impact: Design, Delivery, Evaluation and Policy (0.5) ^#
	PP454 Development Economics (1.0) ^#
	PP465 City-Making: the Politics of Urban Form (0.5)
	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)
Paper 8	1.0 or 1.5 unit(s) from Data Sciences: ^A
	GY460 Techniques of Spatial Economic Analysis (0.5) ^#
	HP434 Methods and Data for Health Systems Performance Assessment (0.5)
	MA402 Mathematical Game Theory (0.5) ^#
	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) ^#
	MY475 Applied Deep Learning for Social Science (0.5) ^#
	ST416 Multilevel Modelling (0.5) ^# ^* (not available 2024/25)
	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) ² or
	ST445 Managing and Visualising Data (0.5) ^# ³ ^*
	In addition, students may choose courses from elsewhere in LSE not listed in these regulations with approval of the Programme Director and subject to acceptance by the course convenor. Availability of a place on a course outside the School of Public Policy and not listed in these regulations is not guaranteed and is subject to space, regulations, and timetable constraints

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 : PP456 can not be taken with PP455

2 : MY472 can not be taken with ST445

3 : 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.