Data, Evidence and Public Policy (PUBL0097)

Key information

Faculty: Faculty of Social and Historical Sciences
Teaching department: Political Science
Credit value: 15

Restrictions: Only open to MSc Data Science and Public Policy students
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module is about how the public sector use experimentation, big data, and machine learning to design and implement policy. Its central objective is to show how adopting evidence-informed and data-driven public policies has the potential to help but also to harm citizens. Rather than fostering deep technical understanding of machine learning, big data analytics, and experimentation, this module aims to equip students with a conceptual understanding sufficient to recognize challenges and opportunities. It draws on examples of local and national governments using experimentation, big data, and machine learning to deliver public services and allocate scarce resources.

听

Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Coursework
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 51
Module leader: Dr Moritz Marbach

Last updated

This module description was last updated on 8th April 2024.

听

Useful links

Sustainability

For a list of modules related to climate change as well as social and environmental sustainability at 果冻影院, type 'climate' or 'sustainability' into the search. Visit Sustainable 果冻影院 for information on extra-curricular activity on sustainability.

Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

听