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
kymanikd sends brownie points to @gskoglind :sparkles: :thumbsup: :sparkles:
@Evaderei indeed: data wragling is tedious but necessary... most of the online datascience projects you find out there, even kaggle, are excellent but they ship pre-processed data to some extent. We don't have that here. Even simple data, as that one for the Survey require some wrangling.
@gskoglind agree with @kymanikd: a man with a mission! Hope we can help!
@kymanikd how is the training? I am really interested, I think it makes a good topic to be commented and discussed here...
@zcassini: apple pies are the best! :)
"Fun" in this case is to obtain reliable, meaningful results after you hard work and having a dataset that allows you to make clear, to-the-point visualizations with easy.
Wrangling is to make the raw dataset accessible to your tools and functionalities, to detect issues that invaliate your analyses, and to facilitate discovery.
@Evaderei unless you have a strong interest in information discovery, statistics, algorithms and data engineering, I think it is better not to get into DS in depth. I would suggest you to get an overall idea instead. I found the Howe's course (the WU coursera training you might be following) one of the best in the area, so take it if what you want is just an introduction.
In data science and data analytics you might not be building an app in several jobs, no. You will develop other products, like algorithms to improve the presentation of a recommender or how better clustering the data or doing a better forecast. But think about kaggle or many other cloud SaaS data-driven projects, if that is what you are interested....
However, think that you can get into data analytics at any point of the route. You can always partner with someone else and complement each other. You might, for example, specialise in building data-driven apps without touching the analytical part. It sounds easy, but it is not: you may require certain level of training in terms of UX, API's, etc to make a good project. (this is why I am in FCC by the way...).
But check your goals: If you want examples of what data scientist and data analysts in general could do, just check the tableau public gallery (I have been doing that just few minutes ago...):
If you don't get excited with what people have been doing in that gallery, or if you don't care about the impact of Tableau as a tool and if kaggle or drivendata don't mean anything to you, if you cannot image yourself building an IoT application, then you should be trying something different.
DSR (DataScience Room) is an effort to gather campers of all levels and specializations willing to engage in discussion, collaboration and practice of data-related projects, with preference for using FCC data.
Tentative DSR site: http://evaristoc.github.io/FreeCodeCamp_DSR/
evaristoc, sarony, jameswinegar, Evaderei, gskoglind, kymanikd, koustuvsinha, profoundhub, zhik, Mashadim, zcassini, QuincyLarson, theflametrooper, erictleung, MarcelSchulz, darwinrc
['https://gitter.im/FreeCodeCamp/GameDev', 'https://medium.freecodecamp.com/being-a-developer-after-40-3c5dd112210c#.p07pixihn', 'http://www.r-bloggers.com/the-one-machine-learning-concept-you-need-to-know/', 'https://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA', 'https://youtu.be/f7vO05pqft8']
(For a longer list check: http://evaristoc.github.io/FreeCodeCamp_DSR/update )
['freq', 'graphs', 'demographics', 'analytics', 'driven', 'activity', 'reddit', 'sandbox', 'survey']
(For a WORD CLOUD check: http://evaristoc.github.io/FreeCodeCamp_DSR/update )
kymanikd sends brownie points to @quincylarson :sparkles: :thumbsup: :sparkles:
kymanikd sends brownie points to @evaristoc :sparkles: :thumbsup: :sparkles: