I would be taking the following modules: 1. Social and Cultural Data Analytics 2. Intro to Quantitative Methods 3. Data Visualization 4. Big Data in Practice: Co-laboraties, Tools, & Methods 5. Big Data and the Law 6. Theorising Big Data 7. Dissertation
On the surface the social research methods degree seems less related to data science, but it covers quite a bit of statistics. However this in the context of social science, not so much Big Data as it relates to industry, and there's less opportunity for coding outside of STATA and R. The courses module selection includes Applied Machine Learning, Applied Regression Analysis, Multivariate Analysis, etc. You're only required to take one introductory qualitative methods module and then you specialise in either the quant or qual stream. This course appeals to me quite a bit because I could go quite in depth with statistical methods, but I worry that the name 'Social Research Methods' makes it less immediately obvious what its relation to data science and analytics might be when applying for my first job out of school. I would be taking the following modules: 1. Research Design for the Social Sciences 2. Qualitative Methods 3. Applied Regression Analysis 4. Applied Machine Learning 5. Multivariate Methods 6. Data for Data Scientists or Causal Inference (Both sound cool) 7. Dissertation The pros and cons of both are as follows
LSE: Pros - Seems to have more prestige from what I read online - Modules seem more rigorous - Looks better on CV? - Basically can be structured as an applied stats master's with a single qualitative module - Stats heavy master's + computer science master's = magic bullet of data science
Cons - More academic and therefore - Less social - "Social Research Methods" isn't as immediately applicable to data science as far as an HR person reading my CV goes
KCL: Pros - Student life seems way better - Bigger university, more rounded university experience - I can study abroad during my summer semester - Focussed exclusively on Big Data - Perhaps gives me a better rounded understand of big data heading into my second master's the year after
Cons: - Modules seem a little bit Mickey - Mouse - MA looks a big 'softer' than MSc - Less technical focus (I've emailed about this - the MA is intentionally interdisciplinary) - Less prestigious
Final point: The year after I'm starting the MCIT at University of Penn, their conversion master's for computer science, and I want to compliment that master's with big data specific training as well. It seems like for data science a lot of graduates of strictly 'data science msc' type courses either lack the statistical skills or coding skills, or are unable to work on real world problems. The computer science master's will give me a better foundation in compsci, and I want to focus on statistical methods in the other master's, and will do so for either dissertation.
I would ideally remain in academia doing research in social data science during my PhD, but there's always the chance this doesn't happen and I want a skill set that transfers to industry as well.
Thank you for any help you can offer... I can't decide!