Comments (2)
+1 to a "How to Contribute" section.
One of the things I am finding lacking is some kind of estimate for each task/course/book on the list. For example the Statistics I Princeton / Coursera class, is going to be a 12 week class - when offered next - and they suggest you will need 5-8 hours/week to complete the course. I'd love to look through all of classes/books/etc and surface some of that information directly into the list. Would this be a welcome pull request?
Similar, but slightly different, It might be helpful to spell out "How to take this Masters" as well. Should my goal be to be able to say "I completed The Open Source Data Science Masters" or should my goal be to say "I created and completed my own Open Source Data Science Masters"? I think I prefer the former as attaching to a group lends credibility, even if that group is an open source community-based curriculum. This leads me to ask about the role of the /transcripts folder - is that something open to any and all, or would I manage my transcript in my own fork of this repo?
from go.
Much of the qualitative feedback offered in the past has been taken into account in the rebuilt OSDSM, pushed late last year. Please take a look and relay any updated feedback! Thank you for your thoughtful suggestions about how to engage folks here.
from go.
Related Issues (20)
- Intro to Data Science not free HOT 2
- consider adding free training resources provided by Google HOT 2
- Graph viz! HOT 4
- Recommendation and clarification on getting started HOT 1
- Resourceful HOT 1
- Add a causality section
- Other indexes
- Cheat sheets HOT 1
- Dead link for Linear Regression in machine-learning.md HOT 1
- Dead Coursera Link for Neural Networks for Machine Learning in specializations.md. It redirects to "Course Not Found" page.
- Dead Link in README.md HOT 3
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- I would love to contribute. . . HOT 4
- Data science master
- spaCy can be added to Natural Language Processing & Understanding HOT 1
- The certificate is expired
- http://datasciencemasters.org/ not working HOT 3
- paypal.
- Good 👍 HOT 1
- Big Data Analysis with Twitter UC Berkeley / Lectures No longer available :(
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from go.