These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
kurumkan sends brownie points to @erictleung :sparkles: :thumbsup: :sparkles:
kurumkan sends brownie points to @mesmoiron :sparkles: :thumbsup: :sparkles:
Greetings DataScience Group. Last night I attended an event at CodeFellows.
Join us for a special Python seminar, where attendees will create a data project in Python!
During this class, we’ll run through the process of taking a data project from conception to data cleaning and reduction, all the way through to creating a visualization. Along the way, we’ll step through some basic arithmetic operations, justifying our choices with the goal of our study and the limitations of our tools.
This seminar will focus on introducing and immediately using tools in Python.
Check out the full outline for the evening: https://codefellows.github.io/data-analysis-glance
It's a pretty good walk-through to get your feet wet in a little data science-y stuff.
apottr sends brownie points to @revisualize :sparkles: :thumbsup: :sparkles:
evaristoc sends brownie points to @revisualize :sparkles: :thumbsup: :sparkles:
In this opportunity I attended this meetup:
Both presentations were interested but perhaps the most interesting one of both was the last one, by a Statistician Prof. at Leiden University, who is also a phylosophist. He reminded the audience about how doing BAD statistics can take us to non-human centred decisions, but biased ones. We have discussed the problem here in this channel, specifically when referring the apparently bias of selecting over higher North American black people recidivism ranking based on machine learning algorithms, which was also used as example in this presentation. Interestingly, there is still no simple answer by people at the level of that professor about how to face that bias. He, as a humanist, suggests though that the best way to avoid the inhuman selection is by simply taking any sensible demographic out of the model. I think this is a fair solution. Here another pair of links about the same topic of last week:
In the same meeting in an after-thought and chat I met a couple of people who are working on interesting projects in Data Science for Universities. One of them is working on a search engine for Data Science trainings between Dutch universities. It sounds simple but the actual technical implementation is tough as the material to be used for the search engine is not in a centralised database but scattered in many different places and formats.
The other person was on an start-up working on a (AI?) platform for education staff fully oriented to learn Math and Science for university students. The software is to help teachers to verify the solution of the problems students post as well as providing them advice about better ways to solve it. It is just like an assistant teacher that shows the required theory when needed but forces to iterate with several solutions until getting the right one. The system "reads" your solution and provides the best advice to your case. I tried the demo: no bad... The key offer of this cloud based tools consists in the following:
Correcting homework and (practice) tests is not necessary anymore (sic). Intelligent feedback helps students where they need it. Our solution provides real personalized learning, even with large groups.
A MOOC mostly contains theory, for example as video-lectures. The strength of our solution lies in our interactive elements, like exercises and tests, which offer immediate personalized feedback where needed. This is something a traditional MOOC can never offer.
It started 2010, so I must say it looks a matured stuff. The format also reminds me another learn-to-program platform developed here in the Netherlands for K-12 classes and that it has been recently deployed.
SoWiSo: have a look if you are just interested: