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So I'm a recent transfer 2nd year student from Computer Science major to Mathematics major. Though I do have a bit of an issue here. I can choose between the applied mathematics, pure mathematics and statistics concentrations.

Along with this major, I'm doing a minor in Data Science with courses focused on Economics and Statistics.

In the future, I'm interested in doing a master's degree in Data Science and a career in data analysis for businesses.

Although I know any degree can be used to get into graduate school, I still want to which would be most beneficial for education in the future as well as opportunities for internships, research, and jobs.

Thank you!

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  • $\begingroup$ A major and two minors is too thin. Why not Computer Science or Data Science undergrad? If math then statistics. $\endgroup$ – paparazzo Jan 7 '16 at 20:03
  • $\begingroup$ Oh no, it's not two minors. I can choose what to focus on in my minor of data science so I'll be taking courses in economics and statistics. $\endgroup$ – user6214 Jan 7 '16 at 20:06
  • $\begingroup$ I'm not too good in computer science taught by professors. I was weak in computer science last year thus causing me to change my major to computer science. Major in data science isn't offered in my school. I'm still gonna do computer science through moocs and online courses. $\endgroup$ – user6214 Jan 7 '16 at 20:08
  • $\begingroup$ @user6214 - I think being "weak" in computer science is going to be a huge deal once you're done your undergrad and want to work. Learning programming is hard. It doesn't get any easier if you ignore it. $\endgroup$ – rocinante Jan 7 '16 at 20:24
  • $\begingroup$ I don't mean to argue here but I'm not ignoring programming, I just decided to go from the straightforward track of computer science to Mathematics. I can code. One of the best things I'm good at coding is the logic, I just can't turn it into code. I can't learn computer science in a 3 month setting. That's why I'm going to learn code on my own pace, individually. $\endgroup$ – user6214 Jan 7 '16 at 20:32
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If you know that you want to become a data scientist, you can pretty much rule out pure mathematics. Note, I'm not saying that pure mathematicians cannot become data scientists, but it's not the most natural transition. Between the other two branches, stats is probably the most natural path. Both will having you think about applying math to answer real world problems, but stats is very specifically geared towards focusing on larger scale data analysis.

EDIT: @rocinante mentions the need for software skills, and suggests CS over econ as a minor. I would say this really depends; if you are in a bigger data science team, you'll probably work alongside dedicated programers, who will be able to implement your analytics more efficiently than you would be expected to as an analyst. If you know that you want to apply D.S. techniques to either finance or business, domain specific knowledge is helpful. Additionally, if you know that it is your desire to end up in data science, you'll be able to look for ways to use programing along the way, and keep those skills sharp.

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If you are considering having data science career you should start developing background on statistical theory, software engineering principles, database, data structure, algorithms development and statistical computing.

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I agree with Gartner about the importance of statics. However, if your data science minor courses focus on economics, I would re-evaluate going through with it. Instead, I would switch to a computer science minor, or use up your electives to take programming courses.

As a person who did applied math and stats, I felt that my programming knowledge was really deficient when it came to handling real life huge, messy data sets. All my program required was two Java courses, so I ended up having to spend an inordinate amount of time learning more about scripting, useful programming languages, etc.

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I recently answered in Why does topology rarely come up outside of topology? that pure mathematics (topology) can be useful in data science. I believe that mixed skills are required for data science: programming (computer science, algorithmics), practicing (statistics, signal processing), theorizing (logic, mathematics). You can get a metromap of data science skills, which are numerous.

metro map of data scientist skills

I believe that it can be better to learn first what is more difficult, and what you cannot easily learn by yourself or with online lecture. A little bit of math seems interesting, it can give you (if properly chosen) fuel for the future, by learning abstractions you rarely learn elsewhere.

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