MSc in Applied Social Data Science
Programme code: TMASDS
Department: Methodology
This information is for the 2018/19 session.
Guidelines for interpreting programme regulations
Classification scheme for the award of a taught master's degree (four units)
Exam subboard local rules
Fullyear programme. Students must take two compulsory courses, options to the value of 2.0 units and a capstone project as shown.
Paper 
Course number and title  

1 
Computer Programming (H) and  
Courses to the value of 0.5 units from the following:  
Data Structures, Databases and Data Sharing (H)  
Managing and Visualising Data (H)  
2 
Fundamentals of Social Science Research Design (H) and  

Courses to the value of 0.5 units from the following:  

Applied Regression Analysis (H)  

Intermediate Quantitative Analysis (H)  

Machine Learning and Data Mining (H)  
3 
Courses to the value of 0.5 units from the following:  

Research Methods for Evaluation in Health, Development and Public Policy (H)  
Applied Statistical Computing using R (H)  
Survey Methodology (H)  
Causal Inference for Observational and Experimental Studies (H)  
Quantitative Text Analysis (H)  
Social Network Analysis (H)  
Distributed Computing for Big Data (H)  
Or any halfunit course from the Methodology Options List below  

AND 


Choice of any other 0.5 unit LSE course (including MY) with approval of the academic adviser.  
4 
Capstone Project 


Methodology Options List
Fundamentals of Social Science Research Design (H)  
Research Design for Studies in Digital Innovation (H)  
Research Methods for Evaluation in Health, Development and Public Policy (H)  
Qualitative Research Methods (H)  
Doing Ethnography (H)  
Qualitative Research with NonTraditional Data (H)  
Qualitative Text Analysis (H)  
Special Topics in Qualitative Research: Introspectionbased Methods in Social Research (H)  
Introduction to Quantitative Analysis (H)  
Applied Regression Analysis (H)  
Applied Statistical Computing using R (H)  
Multivariate Analysis and Measurement (H)  
Survey Methodology (H)  
Causal Inference for Observational and Experimental Studies (H)  
Special Topics in Quantitative Analysis: Quantitative Text Analysis (H)  
Social Network Analysis (H)  
Intermediate Quantitative Analysis (H)  
Computer Programming (H)  
Data Structures, Databases and Data Sharing (H) 
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.