MSc in Applied Social Data Science

Page contents > Methodology Options List | Prerequisite Requirements and Mutually Exclusive Options

Programme Code: TMASDS

Department: Methodology

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 (four units)
Exam sub-board local rules

Students must take two compulsory half-unit MY courses, a dissertation and optional courses (MY and/or non-MY) to the value of two units. The total value of all non-MY courses should not exceed one unit.

Please note that places are limited on some optional courses. Admission onto any particular course is not guaranteed and may be subject to timetabling constraints and/or students meeting specific prerequisite requirements.


Paper	Course number, title (unit value)
Paper 1	MY470 Computer Programming (0.5)
	Exceptionally, students who can demonstrate sufficient prior training in or professional experience with computer programming commensurate with that covered in MY470 can substitute a 0.5-unit course from Paper 3 for MY470. This would be subject to the approval of the MSc Programme Director.
	And courses to the value of 0.5 units from the following:
	MY472 Data for Data Scientists (0.5)
	ST445 Managing and Visualising Data (0.5) ^#
Paper 2	MY400 Fundamentals of Social Science Research Design (0.5)
	And courses to the value of 0.5 units from the following:
	MY452A Applied Regression Analysis (0.5) ^#
	MY452W Applied Regression Analysis (0.5) ^#
	MY474 Applied Machine Learning for Social Science (0.5) ^#
	ST443 Machine Learning and Data Mining (0.5) ^#
Paper 3	Courses to the value of 0.5 units from the following:
	MY405 Research Design for Policy and Programme Evaluation (0.5)
	MY456 Survey Methodology (0.5) ^#
	MY457 Causal Inference for Observational and Experimental Studies (0.5) ^#
	MY459 Quantitative Text Analysis (0.5) ^#
	MY461 Social Network Analysis (0.5)
	ST446 Distributed Computing for Big Data (0.5) ^#
	ST449 Artificial Intelligence (0.5) ^#
	ST451 Bayesian Machine Learning (0.5) ^#
	ST455 Reinforcement Learning (0.5) ^#
	ST456 Deep Learning (0.5) ^#
	OR
	Courses to the value of 0.5 unit(s) from the following:
	Methodology Options List
Paper 4	Choice of any other 0.5 unit LSE course (including MY) with approval of the Academic Mentor.
Paper 5	MY498 Capstone Project (1.0)

Methodology Options List

MY405 Research Design for Policy and Programme Evaluation (0.5)
MY421A Qualitative Research Methods (0.5)
MY421W Qualitative Research Methods (0.5)
MY426 Doing Ethnography (0.5) ^#
MY428 Qualitative Text and Discourse Analysis (0.5) ^#
MY451A Introduction to Quantitative Analysis (0.5)
MY451W Introduction to Quantitative Analysis (0.5)
MY452A Applied Regression Analysis (0.5) ^#
MY452W Applied Regression Analysis (0.5) ^#
MY455 Multivariate Analysis and Measurement (0.5) ^#
MY456 Survey Methodology (0.5) ^#
MY457 Causal Inference for Observational and Experimental Studies (0.5) ^#
MY459 Quantitative Text Analysis (0.5) ^#
MY461 Social Network Analysis (0.5)
MY472 Data for Data Scientists (0.5)
MY475 Applied Deep Learning for Social Science (0.5) ^#

Prerequisite Requirements and Mutually Exclusive Options

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

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.