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dataquest.io
dataquest.io
Very exciting to see this posted here.
I'm the maker of dataquest. I'm a self-taught data scientist/coder, and I wanted an easier way for people to get into the field.
I've been working on it for the past three months, and I'm really excited to see people learning with it.
I chose to teach python because it's one of the best first languages to learn, it's useful outside of data science, and a lot of production data science work is now done in python.
It's missing advanced content, but I'm working on it. Let me know if I can help, or answer any questions!
Vik
Right now, content is the main bottleneck, and I'd love some help. If anyone is interested in talking, shoot me an email (vik@dataquest.io).
In the ...tooltip there's this text
> Hey there, Welcome to DataQuest! If you’ve got any questions or feedba…
I have no idea how to expand that. I can't scroll within it and clicking on it doesn't expand it.
Also it really pains me to see you recommending using a for loop to count list members when there's a perfectly good len function there to do it for you. I can understand the desire to do it from a fundamentals point of view, but it feels overcomplicated in the crime mission.
Edit: Is it ok to like the video bits but hate then stylus? It feels like an actual whiteboard or something that drew less artificial (and noisy) lines would be friendlier. The narration is good :)
I'll look into the variable assignments more. Making content has been way harder than I imagined it would be.
I think I need a better graphics tablet -- I got a cheap refurbished one, and it lags a lot.
You may benefit from staying away of fashionable design trends and focus on usability.
Otherwise thanks for setting this source up, looks great (and very usable besides the problem noted above).
- Python3 makes some concepts, like unicode, division, the print function, etc, simpler to understand.
- Almost all major packages (only scrapy doesn't that I can think of), including the scientific stack, are compatible with 3.
- The trend towards 3 seems to be accelerating. 2 isn't going away anytime soon, but I'd like this content to be relevant for some time.
Ultimately, they aren't that different, and I may have a section listing out what you need to do to switch between them.
The parody site was instantly painful though, I think it was a to do with the grey/green on white; amusing afterwards.
Follow your own gut, but investors/buyers don't care how great your platform is. You have no IP so they can pay someone to build another version. Investors and buyers purchase users.
Get a lot of users on your platform and then consider selling.
That being said, codecademy and code school are both model templates that you could use to turn this into a business if you'd like that route.
You can also find hacker schools teaching data science (such as iron yard - http://theironyard.com/academy/python-engineering/) and see if they'll be willing to add your curriculum to their pre-course requirements. You might be able to get them to have students help build our your curriculum as part of their course projects.
Any kind of media coverage (such as fast co, popular science, etc) will help garner attention and users.
If you are able to get access to current sports data (nba, nfl, baseball, etc) and are able to help teach data science around those data sets you could probably get a lot of motivated but non-cs educated users and media coverage. That is also a feature you could probably charge a subscription for. I'm shooting ideas from the hip, so take them and turn them into something that is more familiar with your background.
Whatever direction you choose to take dataQuest, talk with your potential users and get their feel. Make a decision to move in a direction that'll have minimum push-back with maximum achievement of your desired goals.
Focus on user growth and you'll garner the attention of affluent individuals.
Good-Luck and let me know if you need any help or someone to bounce idea's off of!
Checkout Tuva's incredibly easy to use tools to get an idea of how these concepts can be brought to life for data novices and young learners.
Thanks!
Looking forward to use it
But, because of R's quirks, it's generally easier to write and deploy consistent, good-performing code in python. Python code is also more readable, which makes it easier to collaborate. All of my data science work, including plotting, is now done in python.
I'm working on more advanced content for dataquest, and thanks for the feedback.
R is also a great choice for a language and Revolution Analytics just got acquired by Microsoft.
I don't understand why Python community says it is hard to write great performing programs and deploy with R. It is by far the most used language in data science and statistics (Open Sourced and possible everything else).
If you look at Revolution Analytics and RStudio's Shinny it is very easy to deploy efficient and amazing apps right now.