Recommendations For Noob In Data Degree

Hi!

I am changing fields from a social sciences background into a more stem-driven computer science / data sci one. In an odd twist, my advisors are not from the comp-sci department and cannot advise me on issues I have.

I have been told the future jobs related to my degree will be from Data Analytics / Science fields, and I will be expected to know Python and R 'well' before graduation.

I have taken basic calculus for social sciences (non-simplification Calc 1 basically), but not Calc 1/2/3. I have taken social science stats courses (econometrics, public admin calc), some basic data stats courses (data mining, Analysis using R), but feel wildly unprepared. I effectively have no Comp Sci background.

Most of my peers come from a Comp Sci (pure) or Economics background.

• I had the very minimum requires to get into the program. But I mean, like the equivalency of plain Ody Gear without any augments 'minimum requirements'. Sure I am 119, but that's like it and ppl within the degree question why I am there lol.

Albeit, I currently have a 4.0 from spending TONS of time tying to learn the material.

In an attempt to NOT be useless mathematically, or via coding, I wish to audit courses to make up the gap in my knowledge.

Does anyone have any recommendations for courses I could, or should, take? Any recommendations in general? :)

Thank you,
Senaki

Open source LLMs will be better at writing Python code than any human by the time you finish training to a decent level.

R is used in statistics mostly, and as soon as people accomplish making LLMs good at Python and other more common languages, they will train them in R too.

I don't think data analysis or programming are very AI safe career paths at all.

Data Science is a wide field that sits on top of a stack of technologies that were developing individually overtime.

But overall, its quite easy to grasp the full picture. For that, I would advise you to enroll some course about CRISP-DM, thats also called data mining, but its the basic overview of the entire pipeline.

In that pipeline you will see the more specific fields: IT infrastructure happens heavily in the process of ELT and Big Data.

Data architecture and business intelligence are major part of the corporative subjects related to data.

Math, statistics and programming are the base of machine learning.

Machine learning is the basis of AI.

The current challenge in most companies is integrating AI in the business intelligence to provide quality insights for decisions.

But AI is heavily based on data quality, thats related to the data architecture ELT Big Data processes.

In a nutshell, thats data science, the art of making meaningful use of the zillions data that companies produce.

The job prospects are better than my first degree...(which is basically abject poverty).

Is it worth while to take courses pertaining to Big Data (ex: learning Hadoop), or Data Infrastructure management (ex: SQL courses).

I've noticed a lot of courses seem to focus on ML Algorithms of some degree. Ex: Random Forest, Regression Analysis, KNN, etc. But do not seem to require the math behind the occurrences (at least with regression). Not certain why they focus so much on this.

Those courses are less popular than ML so they tend to have higher demand than the course where everyone is bandwagoning (IMO). But I dont think particularly useful to learn some of those technologies unless thats exactly what you will work with or if thats something you actually want to work with, so you can prepare for an interview and say thats your strong point.

Like I said, ML is a stack build over those math statistics and pregramming.

Being build over those, you dont actually need them to perform relatively well in ML because most of the heavy lifting was already done in those ML libraries, where you just need to write a relatively simple script and have the results.

But going one step further would require you to understand whats behind scenes. But thats like going one step further in direction to a PhD, for example, not for everyday work (ML is a refinement of statistical techniques, model solving is a computation processing using calculus and derivatives).

As a last note, current ML is almost like an art than actual science. Thats because despite having written library to get results, most of its (hyper)parameters comes more from heuristic than a method.

Supposely knowing more about math and statistic can help you create better models, that are more representative of your domain, or that can be solved with less effort/faster. Not knowing them will make your tuning more experimental than theoretical (IMO).

Sadly I'm still too fresh into the degree. I am still not even sure what 'end goal' I want as of yet, so I cannot predict if I will use it or not. :(

Makes sense. I'm going for a MS.


I see


Thank you for all the info. I appreciate it! :)

You don't need R, just Python.

The joke used to be that data scientists are people working in industry as statisticians without statistics degrees. Now the joke is that actually no one cares about statistics and data scientists are just Python devs that write bad code.

I have a BS in comp sci, with a minor in mathematics and a speciality in computer architecture and design, which is basically an intro to computer engineering. I graduated in 2019. These are some of my experiences going through it.


I actually don't think this is an odd of a twist as you think. My advisors while going for my comp sci degree were not in the technology field at all and are mostly there for the bookkeeping aspects of your schedule. My advice here is figure out the chain of command and talk to the lowest rung that has the degree you are after. In my case, I was in the office of the associate chair of the comp-sci department, and one of my regular teachers was the associate dean of technology, and I could go pick their brains about stuff. This sorta depends on how big the school is and all that, but sorting out my specialty woes was a lot easier w/ the chair of the comp sci department instead of the advisor w/ the communications degree.

Edit: Also, I had advanced knowledge that the school was planning a math minor and saw a couple iterations that took, because I knew both the chair and associate chair of the math department.


Are calc 2/3 required for your degree? In my case they were shunted off to the minor, but calc 1 was and it was the great filter. Biggest piece of advice I can give here is they don't really all build off each other in order, 2/3/diff sorta all flow from calc 1, so if you do only ok in calc 2, like I did, you might still be good in 3.


If you learn this way great, but there was no bigger waste of my time than slamming my head into the book. What I ended up doing to reinforce the material was tutoring the classes I had already done. I intially did this with the gen ed classes after impressing the chemistry professor.


This can bite you in the ***. If you're doing it because "job good" you're gonna hate it, and as others have said it's a big field so you can miander into a bunch of different topics. The specialty I ended the degree with isn't the one I started with (which was biometrics), but after getting an advisor to voodoo my schedule to include the assembly langauge class I liked it so much I went back to school to push even closer to computer engineering. The two classes where I picked up a soldiering iron were more fun than half the classes for my degree. It doesn't have to be a super concrete goal, but you are gonna want at least a cardinal direction of where you're going.

When I was deciding my degree I was talking to the chair of the information security and intelligence degree at Ferris here and he has a minor in biology he never uses.

You might wanna pump the breaks here. I had an ex-girlfriend that almost had 3 degrees but switched at the last minute each time, including 1 degree at an expensive Christian school. Combined with above you might go through various options and decide to scrap the whole thing.

I'm not gonna be much help here since you seem to be focused more on data science topics, which I would have merely touched on. Python and R weren't a part of my degree at all though I did learn Python for my internship. My general degree used mostly C#, and my specialty used assembly and C++. The few biometrics classes I took used matlab.

I know a lot of this is Debbie Downer stuff, but I watched people flounder here, get filtered by calc 1 or logic, and I myself changed specialties once. And I'm not much help on the job side of things either, sorry.

Edit: All of this had Veterans Affairs breathing down my neck the whole time, so any sort of schedule changes had to be approved simultaneously by the school and the VA, so you probably have more wiggle room.

Being able to work with people and understand society seem like safer future bets than working with data in the age of AI. I'm pretty sure the "learn to code to get a safe career" train has already left the station.

My post was mostly facetious. A good number of my cohort ended up working entry-level non-BA requiring degree jobs tho. To which they cannot afford to live well now. So I wouldn’t recommend my initial route to anyone spare you speak Mandarin or ‘know someone’.

I have been loving the courses thus far and find them fascinating.

You’d be surprised by the lack of jobs based around what society ‘needs’ vs. why society ‘wants’.

Almost all of my ‘successful’ friends from undergrad changed fields except a select few who went to law school or teaching, or for a PHD. I guess a few spoke ‘in-demand’ languages and went for a high paying government jobs. But the vast majority really did not end up well off. I didn’t want to teach grade school, but was considering PHD when I found because I enjoy research.

Ended up picking this one because I loved stats in undergrad and figured I’d enjoy the field. Thus far I was correct. The level of math is a tad higher than my current tier, and coding is entirely new to me. But I’m getting there. :p

The public LLMs cannot code anywhere near as well as non-coders think they can. They are essentially predictive text algorithms, so if the problem you are trying to solve already exists in a near identical format on a dataset, they'll spit out a great solution. But, since most things need to be integrated with or even built around other existing non-public code, the model will not be able to solve the problems correctly. Further, it won't be able to tell you that it's not solving it correctly, so you may end up putting in code that appears to make sense but doesn't actually do what's intended.

I don't disagree that it's no longer a safe career path, though. LLM will likely be used by the best software developers to reduce the workload. Outsourcing is an issue, too. I was actually looking at getting a teaching certification as an alternative option while I have the free time and money to do additional coursework [dual purpose, my partner teaches so we'd have similar/same days off, but it's about as secure a field as it gets].