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I'm currently choosing between three programs and could do with some advice. My goal after graduation is to start as a DS who can solve business problems and move onto leading analytics/DS teams and strategy down the line. I already have ~3 YOE in Analytics and DS. NYU MSDS: One of the best DS programs and I can shape it too my liking with electives. However, some subjects would be too technical. A lot of proof-based content. Also. It doesn't seem wise to spend 2 years when 1 year courses are available and to go too deep into the theory when I want to move into management in 5 years. Also losing out on 1 year's salary here so program cost automatically doubles. Due to these reasons, Columbia and UT Austin are my primary options. Columbia MSBA: Curriculum is flexible so I can shape it according to my interests. It's quite expensive though. I do have the option of pursuing an internship and TAing to defray the cost a little. Since this is a 1 year program, more if I get an internship, I think this will be slightly less hectic and could provide a break from academic pressure as opposed to Austin. One red flag for me is that the program doesn't seem to have ML in the core courses. There are a couple of electives like MS Machine Learning (1.5 Credits), and Machine Learning for Financial Engineering and Operations Research, but I don't know how in-depth those are, or do they only cover certain topics from the context of FE & OR. I can also take electives from other departments, but not in the first semester, when I'll be applying for internships. Does anyone have a view on this? UT Austin MSBA: Great place, reputed program, and cheaper than the others. Very little flexibility though. It's only 10 months so would be quite rigorous as well. It doesn't cover a lot of stats (bootcamp and self-learning) much. ML and Optimization courses are good though. Also, most people here have 2+ YOE, Columbia would have some recent grads as well. How important is it to study with experienced people? Also, how important are career services? I know Columbia's career services are not really good but most people do get jobs. My thinking is that Columbia is a good option due to the flexibility to choose electives from any school, the reputation, the location, and the internship. It's expensive but I hear people pay back the loans within 3 years. What would you guys suggest? Should I look at any other factors? I'll be grateful for any help. Thanks!

I'm currently choosing between three programs and could do with some advice. My goal after graduation is to start as a DS who can solve business problems and move onto leading analytics/DS teams and strategy down the line. I already have ~3 YOE in Analytics and DS. NYU MSDS: One of the best DS programs and I can shape it too my liking with electives. However, some subjects would be too technical. A lot of proof-based content. Also. It doesn't seem wise to spend 2 years when 1 year courses are available and to go too deep into the theory when I want to move into management in 5 years. Also losing out on 1 year's salary here so program cost automatically doubles. Due to these reasons, Columbia and UT Austin are my primary options. Columbia MSBA: Curriculum is flexible so I can shape it according to my interests. It's quite expensive though. I do have the option of pursuing an internship and TAing to defray the cost a little. Since this is a 1 year program, more if I get an internship, I think this will be slightly less hectic and could provide a break from academic pressure as opposed to Austin. One red flag for me is that the program doesn't seem to have ML in the core courses. There are a couple of electives like MS Machine Learning (1.5 Credits), and Machine Learning for Financial Engineering and Operations Research, but I don't know how in-depth those are, or do they only cover certain topics from the context of FE & OR. I can also take electives from other departments, but not in the first semester, when I'll be applying for internships. Does anyone have a view on this? UT Austin MSBA: Great place, reputed program, and cheaper than the others. Very little flexibility though. It's only 10 months so would be quite rigorous as well. It doesn't cover a lot of stats (bootcamp and self-learning) much. ML and Optimization courses are good though. Also, most people here have 2+ YOE, Columbia would have some recent grads as well. How important is it to study with experienced people? Also, how important are career services? I know Columbia's career services are not really good but most people do get jobs. My thinking is that Columbia is a good option due to the flexibility to choose electives from any school, the reputation, the location, and the internship. It's expensive but I hear people pay back the loans within 3 years. What would you guys suggest? Should I look at any other factors? I'll be grateful for any help. Thanks!

Hi All, Just wanted help in evaluating my profile for Data Science/Analytics/Business Analytics Programs SCORES: TOEFL: 115 GRE: 165(Q) (Worried about this one), 162(V) Work Ex: Analytics - 18 Months - One Research paper implemented + Preparation of analytical dashboards from large datasets to track KPIs Internship(Data Mining) - 2 Months - Sentiment Analysis + Topic Modeling Education: Graduated from a Tier-1 University(India) with 8.46/10 GPA (B.Tech - Engg. Physics) Current Pool: NYU (Data Science), Columbia University (Data Science), UCLA (MSBA), Georgia Tech (MSBA), University of Michigan (Data Science), MIT (MS Analytics), Imperial College London (MSc Statistics (Data Science)/Msc Business Analytics) University of Pennsylvania (MSE in Data Science) University of Texas Austin (MSBA) University of Warwick (Msc Data Science/Msc Data Analytics) Northwestern University (MS Analytics) University of Chicago (MS Analytics) Harvard University (MS Data Science) University College London (Data Science/Analytics) Northwestern University (MS Analytics) The above pool is quite large. I want to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. Any help will be appreciated. Thanks!

Hi All, Just wanted to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. SCORES: TOEFL: 115 GRE: 165(Q) (Worried about this one), 162V Work Ex: 18 Months(Analytics (One Research paper implemenated/Preparation of analytical dashboards from large datasets to track KPIs) + Internship(2 Months(Data Mining Education: Graduated from a Tier-1 University(India) with 8.46/10 GPA (B.Tech - Engg. Physics) Current Pool: NYU (Data Science), Columbia University (Data Science), UCLA (MSBA), Georgia Tech (MSBA), University of Michigan (Data Science), MIT (MS Analytics), Imperial College London (MSc Statistics (Data Science)/Msc Business Analytics) University of Pennsylvania (MSE in Data Science) University of Texas Austin (MSBA) University of Warwick (Msc Data Science/Msc Data Analytics) Northwestern University (MS Analytics) University of Chicago (MS Analytics) Harvard University (MS Data Science) University College London (Data Science/Analytics) Northwestern University (MS Analytics) The above pool is quite large. I want to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. Any help will be appreciated. Thanks!

Looking for answers for a similar profile with an additional 1.5 years of work experience in analytics. No papers/projects under supervisors though.