>>14350486I forgot to say what I actually got out of these. I am now looking at my LinkedIn to remember what my courses even were and I'm trying really hard to think of something I learned in those courses that I use in my day to day life.
I think that there are a few things, but if I remember correctly I knew those things before I saw them in the course. I suppose if I hadn't know that stuff I would have learned it. It was some ways to do statistical analysis in Excel. Something I also learn that I kinda value is the name of the Python libraries that people actually use in data science. That way if I ever get such a job I know which libraries I have to review for 5 minutes before I'm ready for the job. So I suppose I can sum it up like this:
Tangible things I learned:
>A little more Excel than I knew before>The generalities of data scienceThat is probably because I am a little too well-read and I was thinking 'basic' courses in topics where I was already at least 'intermediate' at. However I think what I mostly learned was confidence. The confidence to demistify certain professions.
For example, in industry right now data science is the meme of the day (Nowadays everyone needs a data science department just like 5 years ago everyone needed an app). It will die down but that data science course taught me something important: industry data science is just applying XGBoost to things.
If you get a data problem you:
1) Get the data
2) Clean the data
3) Use XGBoost
4) Scam your employer out of 6 figure salary
It's a very simple thing. And that is something that sticks with me. It taught me that data science roles are not technical roles or research roles. That will serve me if I ever get hired as a VP of Data Science somewhere. I know that I'll just need to hire the most junior people for the lowest salary, give them a crash-course in XGBoost, and then make bank.
