Statistical Programming for Social Data Science
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Program Overview
Statistical Programming for Social Data Science (ECON0128)
Key Information
The module is part of the Faculty of Social and Historical Sciences, with the Economics department being the teaching department. It has a credit value of 15. Restrictions apply, as it is available only to students on the UCL MSc Data Science and Public Policy programme.
Alternative Credit Options
There are no alternative credit options available for this module.
Description
This module provides students with foundational skills in statistical programming using the R programming language, with an emphasis on practical and reproducible data analysis. It equips students with the ability to:
- Clean, manipulate, and transform data using modern R techniques
- Create effective data visualisations for policy and research communication
- Acquire data from a range of sources including databases, APIs, and web scraping
- Apply computational methods such as parallel processing, simulation, and optimisation The module also introduces core programming concepts and good programming practice, including principles of data management and code modularisation and readability.
Module Deliveries for 2026/27 Academic Year
The intended teaching term is Term 1, and it is a postgraduate module (FHEQ Level 7).
Teaching and Assessment
- Mode of study: In person
- Methods of assessment: 100% Coursework (2 assessments)
- Mark scheme: Numeric Marks
Other Information
- Number of students on module in previous year: 38
- Module leader: Miss Yuxuan Ren
Important Information
The catalogue has been updated with key information about the modules that will run during the 2026/27 academic session. Information in the catalogue is subject to change as teaching and assessment arrangements for 2026/27 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 timed assessments and in-class activity will be confirmed by the teaching department for the module.