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MSc in Applied Social Data Science

Page contents > Methodology Options List

Programme Code: TMASDS

Department: Methodology

For students starting this programme of study in 2022/23

Guidelines for interpreting programme regulations




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:

 

MY452L Applied Regression Analysis (0.5) #

 

MY452M 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 ÐÓ°ÉÂÛ̳ 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)

MY421L Qualitative Research Methods (0.5)

MY421M Qualitative Research Methods (0.5)

MY424 Advanced qualitative field methods for researching space and place (0.5) #  (not available 2022/23)

MY426 Doing Ethnography (0.5) #

MY428 Qualitative Text and Discourse Analysis (0.5) #

MY451L Introduction to Quantitative Analysis (0.5)

MY451M Introduction to Quantitative Analysis (0.5)

MY452L Applied Regression Analysis (0.5) #

MY452M 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)

# 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 . Changes to course and programme information for future academic sessions can be found on the .

 
 
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