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https://github.com/kkdroidgit/awesome-bertelsmann-tech-scholarship
A list of awesome Scholarship articles, guides, blogs, courses and other resources.
https://github.com/kkdroidgit/awesome-bertelsmann-tech-scholarship
List: awesome-bertelsmann-tech-scholarship
Last synced: 16 days ago
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A list of awesome Scholarship articles, guides, blogs, courses and other resources.
- Host: GitHub
- URL: https://github.com/kkdroidgit/awesome-bertelsmann-tech-scholarship
- Owner: kkdroidgit
- Created: 2019-11-20T12:03:18.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2019-12-03T07:21:58.000Z (about 5 years ago)
- Last Synced: 2024-04-11T22:06:18.387Z (8 months ago)
- Size: 29.3 KB
- Stars: 11
- Watchers: 9
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-bertelsmann-tech-scholarship - A list of awesome Scholarship articles, guides, blogs, courses and other resources. (Other Lists / Monkey C Lists)
README
# Awesome Udacity Bertelsmann Scholarship Resources
[](https://github.com/sindresorhus/awesome)
A collection of awesome learning resources from [Bertelsmann Tech Scholarship from the Data Track](https://sites.google.com/udacity.com/bertelsmann-challenge/data-track).
Contributions are always welcome!
# Course Information
## Course Calendar
[Up-to-date Information on Scholarship Activities](https://sites.google.com/udacity.com/bertelsmann-challenge/data-track/data-calendar)
## Course FAQs
[Frequently asked questions](https://sites.google.com/udacity.com/bertelsmann-challenge/overview/faqs)
## Orientation
[Slides](https://docs.google.com/presentation/d/1E_dGFNxcNoXV4Ri25nTpgSJtvIC6-p9JlEybab6urjE/edit#slide=id.g1bdfc81a4a_0_0)
## AMA's
Links will be Updated Soon
- [AMA Session 1: Friday, 11/22 at 9:00 AM (PST) | 10:30 PM (IST)](https://docs.google.com/document/d/1QNF44xdK1RPZNzvujugBYVOpbelh5mABaTuX7MMNzMg/edit)
- [AMA Session 2: Monday 11/25 at 9:00 PM (PST) | Tuesday 11/26 10:30 AM (IST)](https://docs.google.com/document/d/1aBTWREdG2LQ2v7F2kT38qa1JkR1EcKg5ihbhbhJSkKY/edit)
- [AMA Session 3: Tuesday, 11/26 at 10:00 AM (PST) | 11:30 PM (IST)](https://docs.google.com/document/d/1p5X6pROb9BSlkVXQ_doo104YAyorraRwx2nG9-x_nuQ/edit)
## Study Groups
[Link to Study Groups](https://sites.google.com/udacity.com/bertelsmann-challenge/data-track/data-study-groups)
## Study Tracker
[Course Overview and Tracker from Tobiloba Adaramati](https://docs.google.com/spreadsheets/d/1Ma2mi_1j5BCHkk6igDx8IqS1HA2kXrorQfRVSoetxXI/)
## Resources
- [Resources](#resources)
- [Documentation](#documentation)
- [Learn Python](#learn-python)
- [Courses](#courses)
- [Tutorials](#tutorials)
- [Books](#books)
- [Blogs](#blogs)
## Documentation
* [Python Official Documentation](https://docs.python.org/3/)
## Learn Python
* [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/)
* [2-Day Python course for Python beginners](https://developers.google.com/edu/python/)
* [Introduction to Computer Science and Programming in Python](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016)
* [Microsoft's Programming with Python](https://www.youtube.com/watch?v=jFCNu1-Xdsw&list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)
## Courses
Some of the Courses shared from the #resources channel that can you take alongside Bertelsmann Scholarship
* [Coursera' Machine Learning Course by Stanford](https://www.coursera.org/learn/machine-learning)
* [CS109 Data Science from Harvard](http://cs109.github.io/2015/)
* [mlcourse.ai is an open Machine Learning course by OpenDataScience. The course is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. ](https://mlcourse.ai/)
* [Statistics - A Full University Course on Data Science Basics](https://www.youtube.com/watch?v=xxpc-HPKN28)
* [Introduction to Data Science](https://www.coursera.org/specializations/data-science)
## Tutorials
* [Learning Python](https://www.freecodecamp.org/news/learning-python-from-zero-to-hero-120ea540b567/)
* [SQL Tutorial](https://mode.com/sql-tutorial/)
* [Numpy Tutorial - Stanford CS231n](https://cs231n.github.io/python-numpy-tutorial)
* [Data Wrangling with MongoDB](https://www.udacity.com/course/data-wrangling-with-mongodb--ud032)
# Books
Books - free and commercial
* [How To Code In Python](https://assets.digitalocean.com/books/python/how-to-code-in-python.pdf)
* [Python For Everybody](http://do1.dr-chuck.com/pythonlearn/EN_us/pythonlearn.pdf)
* [Think Stats Probability and Statistics](http://www.greenteapress.com/thinkstats/thinkstats.pdf)
* [An Introduction to Statistical Learning](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)
## Blogs
* [Data Science Central](https://www.datasciencecentral.com/)
* [KDuuggets](https://www.kdnuggets.com/)
* [Simply Statistics](https://simplystatistics.org/)
* [Analytics Vidhya](https://www.analyticsvidhya.com/blog/)
* [Towards Data Science](https://towardsdatascience.com/)
* [Chris Albon](https://chrisalbon.com/)
* [Data School](https://www.dataschool.io/)
## Stay tuned
Star this repository for futures improvements and general information.