Platform for data scientists

Hacker News

Hi there Hacker News, I've started a side project http://datasourcehub.com which aims to be a platform for data scientists. The project is still in the idea phase so the UI/UX and functionality are all subject to change. Feel free to play around, below is a guest login, and make sure files are content type of 'text/csv'. All data is subject to deletion, it's just a sandbox right now! By reaching out to the Hacker News community I hope to reach expert data scientists and get their feedback. Below are some questions I'd like to answer and some proposed directions that this project can take. Any advice will help! login: guest_account / password: foobar Questions: 1. What core feature/s would data scientists like on a platform made for them? 2. How difficult will it be to get people / organizations to contribute data? 3. Who would be good resources to reach out to initially? Directions?: 1. github for data scientists (shell command wrapped around diff and patch for data files) 2. dribble for data scientists 3. hosting of large datasets (pastebin style) Please reach out to me here or email me (in profile). I will respond to all feedback. Thanks again!

Visit product View on Hacker NewsJul 29, 2013

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, email · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: started, organizations · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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