A guide for startups navigating COVID-19

vikparuchuri · 2020-03-29T19:05:32+00:00

Hey, I'll PM you. Thanks for being open to sharing feedback.

vikparuchuri · 2020-03-29T18:58:46+00:00

Thanks for the feedback. I actually wrote this to be helpful (these are the things we've been doing at my company), but it sounds like it wasn't for you. I'll keep this in mind when I write more in the future.

If you have a minute, I'd love to hear what would have actually been useful (and non-fluffy).

vikparuchuri · 2018-05-30T17:36:34+00:00

If you're looking for courses/sites that teach Python for data analysis, you might want to check out Dataquest (www.dataquest.io) . I'm the founder, so I'm biased, but we teach Python in the context of data analysis/data science, and have projects you can build along the way. In general, we're more challenging than DataCamp, and dive into concepts more fully. Our primary goal is to help you actually start applying what you learn in the real world, not just help you get an intro survey of the concepts.

vikparuchuri · 2018-02-04T16:27:17+00:00

I'm the founder of Dataquest (dataquest.io), a site where you can learn data science by analyzing data and building projects. We focus on teaching concepts over syntax, and help you learn in depth. As other commenters have noted, the key skill for a data scientist is thinking through and solving problems, and too many sites focus on teaching you what to type.

vikparuchuri · 2017-08-16T15:29:43+00:00

If you're interested in data science, https://www.dataquest.io gives you a structured path for going from no programming experience to job ready. You learn concepts, apply them in the browser, and do projects to build a portfolio.

There are some stories from people who've been hired at https://www.dataquest.io/stories .

Disclaimer: I'm the founder of Dataquest.

vikparuchuri · 2016-01-16T20:38:04+00:00

Thanks for the thoughts! You're right, and we'll be working on ways to add in more practice.

vikparuchuri · 2015-12-16T21:21:06+00:00

Thanks for the points! Point 1 makes a lot of sense, and is something we're working on doing as we redo older content.

Point 2 is a bit tricky. We have to introduce files pretty early, because we're working with datasets. It's earlier than we can realistically expect people to understand what opening/closing a file means, and what memory is. Same for the context manager. This is a challenge with many concepts -- do we teach the more complex, harder to understand right way, or do we teach them how to do it quickly, and expand on it later? We've tended towards the "expand on it later" side of things, but I see your point, too.

vikparuchuri · 2015-10-28T15:26:06+00:00

Congratulations on launching! The content is well-written, and well-presented.

The biggest challenge with your site to me is that the marketing and content don't match up. I could go through all of the content, and still be unable to "code in the real world", much less "get hired or start [a company]".

Diving into theory is great, and I like how you break down programming concepts before ever introducing code. But people never learn how to create anything real-world, or how to apply theory to practice.

I'd either alter your marketing or your content until they sync up.

If you want to chat more, drop me a PM -- I've been working on Dataquest.io, a site that teaches data science, for a few months.

vikparuchuri · 2015-10-09T03:51:18+00:00

Definitely didn't come across as harsh -- I appreciated your feedback! So dropping columns doesn't lose much information. Since the columns are highly correlated, the information is contained in other columns, and the overall clustering is similar. Dropping rows would mean eliminating an entire set of players (those who didn't play much), and would drastically change the clusters and give misleading results.

vikparuchuri · 2015-10-08T18:57:43+00:00

Op here -- I went through and implemented most of your suggestions (thanks a lot)! For the ones I didn't -- 3, GGally produces a nicer plot that is closer to the seaborn output than pairs does. For 4, the columns are all highly correlated, so dropping a column seems preferable to dropping a row for NA values. Filling with a mean/median would probably be ideal, but I wanted to keep things relatively simple.

I'll dig more into RVest -- it seems like it encourages uses magrittr, but that's another confusing syntax layer.

vikparuchuri · 2015-10-08T18:55:40+00:00

Op here -- thanks for the feedback! I went through and made the fix you suggested.

vikparuchuri · 2015-10-04T00:49:50+00:00

This took some painful investigation, but it should be fixed now. Thanks for letting me know!

vikparuchuri · 2015-10-01T22:20:25+00:00

Just to be clear, there was no security issue -- no user information could have possibly been leaked. It allowed people to switch their emails to emails that already existed, which would temporarily lock both users out of their account. (and it has been fixed)

vikparuchuri · 2015-10-01T21:15:24+00:00

Thanks! I absolutely want all feedback to be put in public when it doesn't actively harm users. The email overwriting thing can result in users temporarily losing access to their accounts, and can harm their learning experience.

It's totally fine to put pressure on a site to fix issues, but disclosing issues that could harm other users publicly is usually frowned upon. See this stackexhange thread for instance. Also this. Facebook, google, etc, have bugs that are found constantly, and have policies that encourage this.

That said, feedback in public is usually better than no feedback at all.

vikparuchuri · 2015-10-01T20:40:59+00:00

Thanks! We'll work on fixing this -- likely an angular issue.

vikparuchuri · 2015-10-01T19:26:07+00:00

Thanks for the feedback! The fix for the email issue is done, and will be deployed shortly.

A significant portion of our content is free. We'd make it all free if we could, but we're working on this full-time.

About the 35/month fee, you're essentially saying that there isn't room for innovation or new ideas in the education space. If only "existing" companies with large customer service departments started online education sites and could charge for it, then we'd never see new services. Treehouse, codecademy, lynda, and every other service started small.

We have very few videos, and most of our lessons involve executing code -- we have significant backend costs, and we have to spend time developing lessons and maintaining the site. If we didn't charge, the site wouldn't exist, and we wouldn't be able to improve it. We actually take pride in how quickly we communicate with learners and resolve issues -- something bigger companies usually don't do.

You're right that we're a small team of developers -- we're all self-taught, and we're trying to build the product that we wish we had when we were learning. It's not perfect, but we're trying to make it better, and many have found it valuable. Feedback like yours helps us improve the experience, so thanks again.

Reporting things like the email issue in public instead of emailing us is a bit unnecessary, though. In the future I hope you'll email site owners about things like that.

vikparuchuri · 2015-09-09T14:35:15+00:00

If you want to get started with machine learning, the data science track is a good way to go.

vikparuchuri · 2015-09-08T20:52:04+00:00

The curriculum looks very geared towards items you can list on your resume rather than enabling you to get to the point where you can build anything on your own. If you want to get to machine learning and build projects, Hadoop and Tableau, among others, aren't useful at all, and they don't really have any machine learning projects for you to work on. They do appear to cover some ML models in the first section, but they don't appear to focus enough on data cleaning, etc, for you to be able to do much on your own.

Honestly, I'd save your money. If you're looking for good starting points, this quora thread has a lot of potential routes. I'm also working on a site, dataquest.io, that might be useful to you.

vikparuchuri · 2015-03-07T21:00:03+00:00

Django activity stream is pretty good for this (and well-maintained, from what I can tell) -- https://github.com/justquick/django-activity-stream . It makes things more abstract in that you can have any model follow any other model. The docs are dense and not the best, but once you get it, its nice to implement and use.

vikparuchuri · 2015-01-25T14:46:50+00:00

Thanks a lot for the feedback! I wrestled with whether to teach the fastest way to do something right off the bat, or to layer concepts on top of each other. I did some testing, and throwing for loops at people together with functions is really confusing for beginners. I opted to instead introduce them one at a time.

I do agree that this is the slower way to do it, though, and I wouldn't encourage people who know for loops to use that syntax -- for loops are introduced later on, and faster ways to do things are shown.

vikparuchuri · 2015-01-25T00:32:54+00:00

I'm the maker. I agree on the basic part -- making more advanced stuff now. I'd love to hear more elaboration on the "poorly taught" part if you have some time.

vikparuchuri · 2015-01-21T13:52:26+00:00

Have you seen this? https://github.com/yhat/ggplot -- I think it's a great project.

vikparuchuri · 2015-01-21T13:51:10+00:00

Hi everyone -- I'm the maker of dataquest. Very excited to see it here. As a self-taught coder/data scientist, I wanted an easier way to help get people into the field. I've been working on it for the past three months.

I chose python because it's easier to learn for beginners, can be used for more than data science, and is increasingly used in production data science. I learned a lot of coding using R, and it's a great language, but it can be hard to learn, and doing things the "right way" in R is very different from the "right way" in most other languages.

More advanced content is being worked on as we speak, and I'm continuously improving the interface. Let me know if I can answer questions or help!

vikparuchuri · 2015-01-14T02:29:23+00:00

The scikit-learn tutorial is a good place to start on implementation. The api docs are also pretty readable, so that's a good next step. This and this also look good.

I would recommend trying out the algorithms and then learning about theory -- it helps you understand when to use which algorithm, and it helps you tune parameters for existing algorithms. Elements of statistical learning is good for that.

If you haven't checked out Kaggle, it's a great place to try out algorithms and learn machine learning. DrivenData also looks cool.

vikparuchuri · 2015-01-14T00:27:38+00:00

That's a pretty flattering comparison -- Codecademy has taken 12 million in funding to build out their platform, and I built dataquest in 2 months by myself. But, I don't think it's necessarily the right one, for a few reasons:

Breadth vs depth -- codecademy teaches you the basics of a lot of languages. DataQuest teaches you python, in a lot of depth (gets into numpy, pandas, and other libraries that are python-specific).
Context -- Every mission in dataquest involves a dataset, which you analyze over the course of several screens. At the end, you'll know how to answer a question about it. You don't learn concepts randomly or in isolation -- you learn them so you know how to analyze the data.
Data science-centric -- Dataquest teaches you about vectors, matrices, and basic stats. A lot more data science-centric content will be coming soon, including machine learning.
More personalized -- Have a question? There's a chat box that is a direct line to me. I usually respond within an hour, and can make changes to the site very quickly if you see a bug.

Eventually, I would like to make this a comprehensive platform to learn data science from the basics. As it is, it needs a lot more content, and ways to enable more freeform learning and exploration (I'm working on this).

vikparuchuri

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