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0 pointscarlmr8y ago0 comments

I would also like to know more about getting in. I'm confident I could get up and running in a matter of a few weeks working fulltime on almost anything. I just have a difficult time getting in.

Right now I'd like to go from embedded to data science, machine learning. I think I have some useful skills. But I obviously can't show much in terms of SQL knowledge. I've used python pandas for a few internal tools. I have a solid background in statistics from uni, but I feel like after some years in industry, people don't care about your uni background anymore.

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5 comments · 3 top-level

newusertoday8y ago· 2 in thread

why do u want to leave embedded?

carlmrOP8y ago

At least in my industry all the application layer gets done in Simulink. I prefer working with code. I find working with Simulink frustrating, you can't diff properly, simple things are hidden in hard to find configurations. It just doesn't work well with how I like to work.

Three other option is going back to firmware, which I did in my previous job, but I don't want to write more firmware drivers. It's just not exciting.

Additionally I'd also like to do more creative work, I find data science sounds very exciting and I could probably bring a lot of software skills to the team

But convincing the industry to go another path is probably harder than switching fields.

ultrasounder8y ago

Please don't fall for the "sexiest job of the 21st century" adline.The wife works as a sw dev on a speech rec team(one of the popular ones) and she works with P.hD "Data Scientists" which are looking at getting in to dev. If you apply the pareto rule, "Data science" is 80% Cleaning and 20% ML. Hardcore ML research is totally different which requires atleast a MS with ML focus and pedigree plays a huge part. Here is a thought. Why don't you try rewriting stuff you did with Simulink in Python. My man, Downey got you covered https://github.com/AllenDowney/ModSimPy. I would recommend that you start with Think Python before tackling this book.

cthrow8y ago

If you work in embedded, start to shift your focus into IoT. Internet-connected systems are the ones producing and storing the data necessary for data science/ML to happen. Experience with the data ingestion layer IoT cloud platforms (AWS IoT, GE Predix, Azure IoT Hub) could help you transition to higher level data analysis roles, especially if you can demonstrate your domain expertise with embedded systems. If you are working with Matlab along w/ Simulink, you should check out ThingSpeak: https://www.mathworks.com/products/thingspeak.html

If you are working deep embedded/legacy products or purely on the hardware side, you may need to grab an ML course/certificate to signal your interest, or just network really hard. Having embedded engineering skills can't hurt though.

heurist8y ago

Everyone wants to do machine learning; you need to fight for it to get there.

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