Managing a transition into software and/or data science in your 30s

The Something Awful Forums > Discussion > Ask/Tell > Business, Finance, and Careers > Managing a transition into software and/or data science in your 30s

Kurt Loadeater: May 15, 2006; by Jeffrey of YOSPOS

Magnetic North posted:

My boss says that in the next year or so, he wants to start a Machine Learning / AI project. So I've been looking into it in the hopes of being prepared and being able to be a part of it and use it as a means to enhance my career prospects and learn cool stuff. However, while looking into it, there is a lot of linear algebra under the hood, which I never learned. On the one hand, it would arguably be better to really get an understanding of it, even in a relatively short run-up. On the other hand, if we're just going to use some existing open-source library, does it even matter?

I know learning the math would be better, but I am curious if goons think it is worth bothering.

Understanding LA is a huge plus for any programmer. Anything involving images, representing categorical data, NLP, vectorizing problems will rely on it. Just take a course or two at Khan's and maybe one of Andrew Ng's for reinforcement and applications to plant a seed in your head. IMO it's just one of those things that should be in the toolkit of any developer, especially anyone getting into ML. I can't overstate how important LA has been to me, but I can give you some anecdotes if you're curious.

# ¿ Nov 24, 2019 05:58

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# ¿ May 22, 2024 17:28

Kurt Loadeater: May 15, 2006; by Jeffrey of YOSPOS

JIZZ DENOUEMENT posted:

When I was in grad school, I really liked my courses on program evaluation and regression analysis. Stuff like STATA.

Are there any good accreditation or certificate programs? I want to; refresh old memory, learn new skills, and have a tangible thing to show employers that I know how to do program evaluation.

Creds/certs, probably no. I'd probably try to start with driverless AI just to get a feel for what's currently being offered from that perspective, like h2o, data robot, or azure's ML workspace. The latter is rough but it has some promise, with some explanatory functionality in preview mode. Then branch out into tidy looking Jupyter notebooks on a Github somewhere, hopefully focused on real world/business use cases. Get some experience deploying trained models to the cloud and lambda/serverless functions in general.

# ¿ Nov 24, 2019 06:04

Kurt Loadeater: May 15, 2006; by Jeffrey of YOSPOS

mearn posted:

I'm currently going through a Master's program in Data Science. It's an online program, which isn't really ideal and honestly I haven't found much of the material to be much more informative than a DataCamp subscription would be. I know an online program isn't really the best choice. I've got two semesters left at this point and it's all being paid for by my current employer, so I'm going to stick it out. I guess my plan from here is to just work on personal projects and try to build a portfolio in the meantime, since I'm not sure how much value this degree is going to have on my resume.

Missing out on the networking opportunities of a physical masters program is a definite drawback and that's what I'm trying to figure out how to overcome now. I found a monthly Python meetup in my area, but it looks like most of their recent events have been more focused on Django and other topics that aren't necessarily data-oriented.

Being able to quickly stand up some API is a huge plus for anyone in the field, and some of those folks are awesome programmers. I work as DS lead for a design/prototype studio and the best programmer we have is some guy who loves doing everything in Django. I've learned a lot from him.

Learn just enough from that crowd and you can add 'deployed custom ML models to cloud API for real time analytics and what if scenario analysis' to your resume.

# ¿ Nov 24, 2019 06:09

Kurt Loadeater: May 15, 2006; by Jeffrey of YOSPOS

Kim Jong Il posted:

That's a weird quote, as Hive is widely used.

It's true that there's an oversupply at one specific point where people have been susceptible to get rich quick schemes, but there is massive undersupply elsewhere *at the entry level*, and massive undersupply at the higher levels. There are 100 people applying to one opening because in many cases, they aren't qualified.

And the piece is spot on that a ton of your team is spending doing data cleaning type things which honestly, a lot of the modeling/machine learning types are often bad at. But that doesn't have a whole lot to do with a CS degree. She has a very skewed viewpoint towards tech which is not representative of the entire field. Her advice at the end is mainly good.

Mostly a good article, though. I think many customers have been sold on ML/AI, but in reality haven't even begun to clean and bring their data together. Add in high expectations of what ML/AI can do and they think the failure is on the practitioners, when in reality they aren't even collecting the right data in the first place. For organizations with an advanced data platform and analytic capability already, the right data scientists will continue to add value. For the rest - there's quite a ways to go. Sure writing Python code and SQL queries is easy, but the low hanging fruit is gone.

It's still back to basics for many 'data scientists' - cleaning and engineering data, data lineage, basic visualization, and quickly prototyping ML/AI applications with the latest cloud technologies.

# ¿ Nov 24, 2019 06:59

The Something Awful Forums > Discussion > Ask/Tell > Business, Finance, and Careers > Managing a transition into software and/or data science in your 30s