Masters in computer science or Masters in applied statistics?

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Discussion Overview

The discussion revolves around the choice between pursuing a Master's degree in Computer Science (CS) or Applied Statistics for a career in machine learning and smart data. Participants explore the implications of each degree on career opportunities while considering their current educational backgrounds and professional experiences.

Discussion Character

  • Debate/contested
  • Exploratory
  • Technical explanation

Main Points Raised

  • One participant expresses uncertainty about which Master's degree would be more beneficial for a career in machine learning, noting their background in computer science and math.
  • Another participant shares insights from a professor in Computing Science who emphasizes the importance of a strong mathematics background for students entering machine learning, suggesting that many lack adequate preparation.
  • It is mentioned that the level of mathematics required varies depending on the specific area of machine learning one intends to pursue, with theoretical work demanding a deeper understanding of mathematics.
  • Some participants suggest that a Master's in Statistics may be advantageous for those interested in the theoretical aspects of machine learning, while others note that a CS degree can also lead to opportunities in the field.
  • A participant indicates a desire to understand the theoretical foundations of machine learning while primarily focusing on practical applications, and mentions exploring online courses to aid in their decision-making.

Areas of Agreement / Disagreement

Participants express differing opinions on the relative value of a Master's in CS versus Applied Statistics for machine learning careers. There is no consensus on which path is definitively better, and the discussion reflects a range of perspectives based on individual goals and backgrounds.

Contextual Notes

Some participants highlight the importance of mathematics in machine learning, but the specific requirements and relevance of different mathematical topics remain open to interpretation. The discussion does not resolve the question of which degree is superior, as it depends on individual career aspirations and areas of interest within machine learning.

trickslapper
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Crap. I couldn't get everything i wanted in my topic title.

Anywho, I'm wondering which masters degree would be better for a career in machine learning / smart data while still keeping other - traditional options available? I would love to do a double major and earn both graduate degrees but I'm pretty sure that's impossible with a full time job (if it's not impossible it will take too long for me).

I've read a lot about this and I have mixed feelings. I've seen some people say that if you have an undergraduate degree in computer science then a Masters in Statistics/Math/Physics leads to a powerful combination.

What would you guys suggest? If it helps i have my bachelor degrees in computer science and math and currently work as a software developer (so i plan on going to school part time for right now)
 
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My significant others Father is a professor at the University of Alberta in Computing Science. He publishes a lot in Machine learning. Formally, he is trained in Applied Mathematics and I think he considers this a huge advantage. He takes on a lot of graduate students and I think he is often frustrated with the level of math students enter with. Many students coming from computing science graduate programs lack the proper mathematics background and he spends a lot of time teaching them mathematics or giving them mathematics to learn. That is just one perspective though.
 
Theorem. said:
My significant others Father is a professor at the University of Alberta in Computing Science. He publishes a lot in Machine learning. Formally, he is trained in Applied Mathematics and I think he considers this a huge advantage. He takes on a lot of graduate students and I think he is often frustrated with the level of math students enter with. Many students coming from computing science graduate programs lack the proper mathematics background and he spends a lot of time teaching them mathematics or giving them mathematics to learn. That is just one perspective though.

What level of mathematics do you need to be able to have such a career in machine learning?
 
That link is awesome. I think i'll go through the rest of the pdfs on that website. But back to my original question.. do you think an MS in CS or Stats is better for a career in machine learning? I would love to get both of them and sometimes i lean towards the Stats and then other times i lean towards the CS.
 
trickslapper said:
That link is awesome. I think i'll go through the rest of the pdfs on that website. But back to my original question.. do you think an MS in CS or Stats is better for a career in machine learning? I would love to get both of them and sometimes i lean towards the Stats and then other times i lean towards the CS.

Do you hope to work on the more theoretical side? Then maybe statistics is better. The people I know have a PHD in either statistics or probability (or combined). But It's not like it isn't possible with a Masters/PHD in comp sci. The professor I know takes on many masters and pHD students on topics related to machine learning through the CS department. A neat option (possibly very beneficial) is to get a masters in statistics/probability (you could even focus on something related to machine learning), and then go for a pHd in a computer science department !
 
I don't hope to work on the theoretical side. But i would like to know some theory so i sort of understand what I'm doing when i do hands on work (hopefully one day). I added a couple of classes to my coursera watchlist that relate to machine learning/data science so maybe those will help me make a better decision.
 
THose will, and you should ask the professors too for those classes if you get a chance. Knowing the theory can be a huge advantage, although my opinion is a little biased- coming from pure Math. Good luck : )
 

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