Career transition and academic aspirations: Columbia MA QMSS or LSE MSc Applied Social Data Science?

[edwardsaid]

Hi, I'm currently facing a dilemma. I just got admitted into two prestigious master's programs in quantitative social science: Columbia MA in Quantitative Methods in the Social Sciences and LSE MSc in Applied Social Data Science. I'm a 2019 graduate from a top 10 liberal arts college who majored in International Studies and I intend to transition into a career as a data scientist or a data analyst, preferably in healthcare tech or in fintech (I'm currently an intern at a prominent national security think tank in DC and I just realized it's not for me). I applied to these programs also because they provide good preparation for a PhD in political science, which I'm interested in pursuing later. My question is considering my background and interests, what do you think is the more appropriate program for me?

I list some pros and cons for each program:

 

Columbia QMSS

Pros:

1. Great variety of classes. Very flexible and interdisciplinary. Allows me to specialize in data science while still taking a polisci methods class.
2. Small program size with dedicated faculty 
3. Research assistantship opportunities
4. Access to Data Science Institute and career fairs
5. Great track record of sending graduates to top PhD programs

Cons:

1. Expensive (~$80k tuition)
2. Cost of living in NYC

 

LSE Applied Social Data Science

Pros:

1. Solid curriculum. More tailored towards a career in data science
2. Half the price of QMSS
3. Small program with a dedicated research cluster within LSE
4. Access to LSE's international network (esp with Europe and proximity to finance in London)

Cons:

1. Very young program. Doesn't have an established alumni network in industry and academia. 
2. Research project component does not match rigor of QMSS's thesis component, hence may not be as good of a prep for PhD
3. Unclear of how the degree would be received by potential employers

 

[edwardsaid]

reupping this. Your opinion is much needed!