I graduated with my computer science degree in 2023. At the time I had the mentality that I would study very generally so that rather than focusing on one thing, I’d learn about various different things then I’d learn specifically what I needed to know on the job. What a 2021 mentality! Now people want their SWEs to come out of school specialized with a lot of credentials that my college doesn’t even offer courses on (I’d kill for a course on React or AWS/Azure).
After a lot of fruitless job searching (And working as an AI tutor for a bit) I ended up recently taking a tech support job, which definitely sucks. I’ve been thinking about going back and getting my masters and I’m not sure what to pursue, but I realized that I run statistics and data on my hobbies and get really into and enjoy that so I thought I should debate Data Science. I am in a unique position where my grandmother set aside some money in some education account which sat untouched for 50 years and the only thing it can be spent on is my education, so the masters degree would not be an issue for me financially.
I am interested to hear from Data Scientists who aren’t doing machine learning what does your job consist of? How deep into the mud do you need to get on the mathematics? How much optimization (to make your code run faster, not to optimize a situation) do you have to do? Especially low level optimization.
Finally, I’d like to ask if you think I should continue to consider data science, with a few extra bullets of information!
>I found linear algebra super exciting in college and enjoyed statistics as well, but I struggled a lot with calculus. I would consider myself awful at differential equations, though I’ve never tried to solve one with code before.
>The only class in college that I straight failed was a parallel programming and optimization class. Low level optimizations just don’t make any sense for me.
>I have always found matrices difficult to manipulate with in a code environment. It’s very hard to debug errors in a gigantic matrix or to process matrices in strange ways and I never really figured out the right approach to this. Something about massive matrices makes my mind boggle and I can’t get a grasp on the right angle of attack to start isolating the problem.
If not Data Science, what other masters degree might be good to get as an SWE? Should I just continue grinding the applications game? I just feel so lost ;_;
submitted by /u/Ysehporp
[link] [comments]
r/cscareerquestions I graduated with my computer science degree in 2023. At the time I had the mentality that I would study very generally so that rather than focusing on one thing, I’d learn about various different things then I’d learn specifically what I needed to know on the job. What a 2021 mentality! Now people want their SWEs to come out of school specialized with a lot of credentials that my college doesn’t even offer courses on (I’d kill for a course on React or AWS/Azure). After a lot of fruitless job searching (And working as an AI tutor for a bit) I ended up recently taking a tech support job, which definitely sucks. I’ve been thinking about going back and getting my masters and I’m not sure what to pursue, but I realized that I run statistics and data on my hobbies and get really into and enjoy that so I thought I should debate Data Science. I am in a unique position where my grandmother set aside some money in some education account which sat untouched for 50 years and the only thing it can be spent on is my education, so the masters degree would not be an issue for me financially. I am interested to hear from Data Scientists who aren’t doing machine learning what does your job consist of? How deep into the mud do you need to get on the mathematics? How much optimization (to make your code run faster, not to optimize a situation) do you have to do? Especially low level optimization. Finally, I’d like to ask if you think I should continue to consider data science, with a few extra bullets of information! >I found linear algebra super exciting in college and enjoyed statistics as well, but I struggled a lot with calculus. I would consider myself awful at differential equations, though I’ve never tried to solve one with code before. >The only class in college that I straight failed was a parallel programming and optimization class. Low level optimizations just don’t make any sense for me. >I have always found matrices difficult to manipulate with in a code environment. It’s very hard to debug errors in a gigantic matrix or to process matrices in strange ways and I never really figured out the right approach to this. Something about massive matrices makes my mind boggle and I can’t get a grasp on the right angle of attack to start isolating the problem. If not Data Science, what other masters degree might be good to get as an SWE? Should I just continue grinding the applications game? I just feel so lost ;_; submitted by /u/Ysehporp [link] [comments]
I graduated with my computer science degree in 2023. At the time I had the mentality that I would study very generally so that rather than focusing on one thing, I’d learn about various different things then I’d learn specifically what I needed to know on the job. What a 2021 mentality! Now people want their SWEs to come out of school specialized with a lot of credentials that my college doesn’t even offer courses on (I’d kill for a course on React or AWS/Azure).
After a lot of fruitless job searching (And working as an AI tutor for a bit) I ended up recently taking a tech support job, which definitely sucks. I’ve been thinking about going back and getting my masters and I’m not sure what to pursue, but I realized that I run statistics and data on my hobbies and get really into and enjoy that so I thought I should debate Data Science. I am in a unique position where my grandmother set aside some money in some education account which sat untouched for 50 years and the only thing it can be spent on is my education, so the masters degree would not be an issue for me financially.
I am interested to hear from Data Scientists who aren’t doing machine learning what does your job consist of? How deep into the mud do you need to get on the mathematics? How much optimization (to make your code run faster, not to optimize a situation) do you have to do? Especially low level optimization.
Finally, I’d like to ask if you think I should continue to consider data science, with a few extra bullets of information!
>I found linear algebra super exciting in college and enjoyed statistics as well, but I struggled a lot with calculus. I would consider myself awful at differential equations, though I’ve never tried to solve one with code before.
>The only class in college that I straight failed was a parallel programming and optimization class. Low level optimizations just don’t make any sense for me.
>I have always found matrices difficult to manipulate with in a code environment. It’s very hard to debug errors in a gigantic matrix or to process matrices in strange ways and I never really figured out the right approach to this. Something about massive matrices makes my mind boggle and I can’t get a grasp on the right angle of attack to start isolating the problem.
If not Data Science, what other masters degree might be good to get as an SWE? Should I just continue grinding the applications game? I just feel so lost ;_;
submitted by /u/Ysehporp
[link] [comments]