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I just graduated undergrad with a Bachelor in Science in Economics with a Minor in Business. I hope one day to get a Masters in Computer Science as throughout undergrad I self taught myself programming and got a job in finance at a very respected hedge fund where I'll be combining my finance and coding knowledge (essentially an analyst position where I'll be doing data analysis to support traders at the firm.)

The job will 100% pay for grad school after 1 year and I want to go for a program in Computer Science or Data Science. I'm wondering what I need to do to prepare for this and what programs are available to me given that my undergraduate degree is in a unrelated subject.

I have an overall GPA of 3.3 due to a particularly poor performance freshman and sophomore year and doing especially poorly in Spanish but I received a GPA of 3.7-3.8 my Junior and Senior years while taking a more advanced and quantitative course load.

I've taken 2 elective classes in computer science and received As in both of them although neither class is a core class for a comp sci curriculum.

I also have around 4 years of self-taught coding experience both in my private life and through several internships that I participated in while at college. I'm proficient in several languages (Python, VBA, Java, and SAS).

What type of programs can I expect to be accepted to and what can I do to increase my chances of success? I've heard that I should take ad-hoc core programming classes and study hard for the GRE but apart from that I'm lost.

  • Welcome to Academia SE! Some of the answers, e.g. about lower grades, will be found in a previous question, about admissions to U.S. Ph.D programs. A lot of your question is pretty subjective, though the key question of what to expect from a masters degree in CS vs. Data Science, and flexibility of prereqs for CS masters programs, may be relevant. – cactus_pardner May 6 '18 at 22:28
  • And for what it's worth, depending on how solid your econometrics background is, you may be ahead of the curve on some of the data science concepts, but you may especially want to learn machine learning (probably in Data Science and CS courses) and either theory about how calculations are best done, architecture for databases, etc. (CS) or more about hacking together workflows (Data Science). Finally, you'll probably want to learn R ahead of time (annoying syntax switch from SAS) in either case. – cactus_pardner May 6 '18 at 22:31
  • Thank you for the valuable advice. The link you sent was very useful. Not sure if you have experience coding but do you recommend learning R on top of Python for datascience? I've heard R is better in certain scenarios but from what I can tell they are similar languages. – TheSaint321 May 6 '18 at 22:35
  • I would guess that most curricula would use both R and Python, especially for R's graphing tools. However, I think R vs. Python is probably a way off-topic debate for this SE. :) – cactus_pardner May 6 '18 at 22:39
  • norvig.com/21-days.html is useful to read. – Basile Starynkevitch May 7 '18 at 5:02
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This should be a comment but I need a little more space. Also, the question should be closed as duplicate of who knows what. I know I've written this before but I still need to find some system for keeping track of the common answers I've written.

Step one is to look around and pick one or more subfields that appeal to you. Then, pick a couple of specific programs (a program means a department in a particular university) that make your mouth water. Do this by reading some of the professors' bios and research program descriptions. You can also look at grad student bios and perhaps also some of the professors' publications.

Once you've got your eye on a particular program, look at their program of studies (requirements for the degree) and the course descriptions in the catalog. Make sure you understand what's a prerequisite for what. You may need to go backwards a bit in your reading, to trace through the undergraduate prerequisites as well. (Check the schedule of classes too, because some departments list phantom courses in their catalog which are never actually offered, and you should ignore those in your analysis.)

Compare what you see with what you already learned in coursework, on the job, in internships, and in self-study. Now you have a list of what it would be good to cover prior to stepping into the classroom in your new field as an incoming grad student.

If you discover some question during this analysis, do not hesitate to contact the department.

Once you know what you are missing, you can of course do some self-study, but I would actually recommend that you take at least one course formally, if only online. You can enroll as a non-matriculated student.

When you are applying you can present as many transcripts as you have.

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