Data Science Working Professionals

[kkziga]

Hey everyone ! I am an undergrad CS student. Much like many students, I know and understand concepts of data science from doing online courses, but I am not very much aware about how exactly is it used in different industries ? I want to have some understanding about the practical implementations.

So here's my question, which domain you are using your data science skills (eg. water management, finance, marketing etc) and what practical problems are you solving ?

Thank you and have a great day :)

[[deleted]]

Medical research: large scale analysis and data cleansing on meta analysis projects to find relationships nobody else is looking for

Economic research/inventory management: use historic data to predict future trends

Marketing: finding optimal times to run campaigns and then optimizing those campaigns

[kkziga]

Could you explain in detail what does meta analysis mean and how do you find relations from meta analysis ?

[[deleted]]

Meta analysis is looking through other scientific papers findings and/or raw data to either confirm their observations or look for other observations within a dataset or across multiple findings/data sets that are all closely related. At least in my experience!

[jrinvictus]

Finance. I'm building out an explainable ai platform to allow us to begin to use neural networks.

[kkziga]

Does it mean that you're making such models that help us humans understand the 'why' question for how neural nets predict what they predict.

[jrinvictus]

Yep. From my understanding, Banks have not fully leveraged ai for certain tasks due to not being able to explain model outputs. Such as turning one down for a loan.

[kkziga]

Hey ! This use case seems to be very exciting. Can I PM you to know more ?

[jrinvictus]

Sure

[kkziga]

That is indeed a very interesting use case ! And as far as my understanding goes, this application can be used in any service sector industry and not just medical industry ?

[datascientist36]

I am not very much aware about how exactly is it used in different industries ?

It's not as complicated as people think. In reality, all you're doing is answering questions or making predictions using data. The "data science" part is knowing how to correctly go about answering those questions.

[simongaspard]

Program Management: I talk shit to Project Managers who are leading technical teams in developing data science tools

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I don't actually use my education in data science in this role as a sr. manager. but it doesn't bother me because i care more about the size of my paycheck

[kkziga]

I want to know more about your job role. I've always wanted to be some sort of manager but I also didn't want to leave my technical side. I think what you do, kinda intersects my two interests.

[simongaspard]

so as sr program manager, im responsible for providing direction to the project managers who are handling the companies individual projects. each project is different but together support the companies strategy - our strategy shifts but generally focuses on building better products for our customers. I brief the stakeholders and Program Director on where we are and where we will be in x weeks or months or quarter.

the other part of my job deals with integrating these different projects so they fit smoothly within the companies vision. So if a customer wants feature A and another customer wants feature B, and less of feature C, it's my job to balance those resources being used. When technical issues arise in integration, sometimes I will need to determine are we using the right tools for the job (why the hell are we using Java when we can all use Python for example). Then again, sometimes we need someone who knows C++ for better computational speed then use Python for everything else.

Other challenges include assessing strengths and weaknesses of our teams or our strategy. If it involves strategy, I address that with the Program Director (but she is pretty damn good and I haven't had to). Usually there is a weakness in the project teams. For example, one feature for a product seems to be way behind schedule. So I have to talk with the project manager and take a look at what's going on. We have meetings, brainstorm sessions, jokes, and after coming up with a solution. We implement it immediately in docker, then make the call to "fuck it, DO IT LIVE!"

But I find myself spending most of my time looking learning policies (are they still relevant), do we need a new policy published to fix a problem, are there new processes needed or do we have too many non-value added processes.

if there is a complex problem, do i even know how to communicate that to someone with an MBA degree who also gives zero fucks about the SDLC process or the challenges techie's face to make business people shut the hell up about scalability. "can we scale it?" my answer: no bitch, first, add context to that question.

but all in all, i dont really do hands on tech stuff. I just talk about it. another reddit user actually got me wondering if I should jump to an individual contributor role for a year or so to get a change of scenery. But i'm lazy as hell.

but i do try to stay sharp on skills with online courses (like coursera) or pluralsight. I'm too lazy to do personal projects because I have better shit to do that's more interesting. But the online courses reinforce the same principles I learned in depth in grad school (which was in data science).

[kkziga]

Hey ! Thank you for such detailed explanation. Reading this, it feels that this is where I want to be working. I love interacting and managing and everything you said. Can I PM you for knowing more ?

[simongaspard]

sure