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Has anyone else begun to resent data science?
today I choose violence
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I just did the Coursera courses by Stanford/Andrew Ng for Machine Learning and Deep Learning. Absolutely amazing content and he steps through it so eloquently that even beginners can stay up with him and he gets really deep too. Great hands on exercises too. Highly recommend for anyone looking to learn data science. Good luck and let me know if you find other courses that are equally good too.
Yes for now- I am new to the field but have to say I am able to hold myself and even challenge the teams (some very good data scientists) based on these two courses. But do the Hands-on exercises yourself so you really understand it. I have not coded for 15+ yrs but still did it so I understand it.
May I ask why do you want to break into ds? Is it the pay? Thanks.
Just start doing analyses at work. You've already got the pipeline stuff down from your DE background, so take literally any data mining class and you're off to the races. As you do that, also take an econometrics class or two. All this is youtube-able btw.
And don't worry about deep learning for now. It's a cool buzzword, but is only useful for a relatively small subset of opportunities.
Long term, get an actual grad degree. Can be a hybrid masters program like an MSBA or an MSDS, but make sure it's stats or ML heavy.
Why data science?
I am leading large data engineering team to support traditional analytics and now most of new RFPs we are fielding are focussed on data science, AI/ML work. In no time I will find myself managing those teams and delivering work. I will have experienced leads helping me to run the that team but I am hands on person and prefer to know details of solutions we are proposing and why. I figured why not start practicing it while we are still remote.
Same as what I said earlier, just get hands on with analyses. Best way to get comfortable with basics of different types of work is to drill them in by doing them. You'll build intuition that'll translate to more complicated stuff.
Though I walk back what I said about not worrying about DL. Your teams will probably throw some of that out (often unnecessarily), so yeah, you'll want exposure. If you want to speed run it, do fast.ai. Otherwise, Ng's classes suggested previously are good indeed. Either way here though, the "doing" still applies.
OP- What approach/methodology do you use on projects Agile or waterfall?
Agile. But it's hard to implement when you are building data layer of analytics application. Make sense when you are dealing with small datasets in 4-5 sprints. But that's the rage now a days so have to get creative.