
RedBT
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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!
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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!
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- data sciece
- ut austin
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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!
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- data science
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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!
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- data science
- analytics
- (and 3 more)