These are chat archives for FreeCodeCamp/DataScience

Oct 2016
Oct 10 2016 08:28
right @alicejiang1
Hèlen Grives
Oct 10 2016 20:13
@alicejiang1 I've done the DS MOOCs on Coursera; however I also took the introductory course on statistics. For simple DS what most people use is actually no more than the 5 summaries when exploring the data. I found R not that hard. However one need to get used to the syntax a bit. But that said, the advanced statistic topics; yes they need some extra study. For a person to succeed there are enough open course ware books that can shed a light on it. Next step is doing the advanced MOOCs. You can teach R at university research level or at highschool level. There is a difference between heavy tech industry and just small to medium sized companies that want to explore some data. Untill now I haven't seen much impressive use of DS in most of the companies. There's still much advocacy/ work to be done. Statistical literacy is not that high on average. As soon as people sense math; they lose interest.
Eric Leung
Oct 10 2016 22:02


Balancing between time consuming coding and math is like juggling

I agree! To combat this, I've thought about doing both, coding the math you learn. I've been meaning to brush up on linear algebra and found this course on Udacity . Looks like they plan on teaching you to write your own linear algebra library. Coding something will really test if you know something.

So in my opinion, bringing equations and the math out from being just static symbols and numbers to more interactive functions you can play around with, you can interact and understand the implications of different values in those equations. I hope that helps.

Eric Leung
Oct 10 2016 22:10
@alicejiang1 sounds similar to Kaggle, albeit not with "data" in the name. It's more for financial data.
Darwin RC
Oct 10 2016 23:59
@alicejiang1 perhaps