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33 changes: 33 additions & 0 deletions onboarding/software_dev.md
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## Installation Guide
This is a guide on what software tools you need depending on your role:

### All roles
- Discord: Connect to the SFL discord
- Gmail: access your SFl gmail for email updates and notifications
- Github


### Software developer
- Required Packages
- Recommended tools
- Resources to get started
- React tutorial : https://www.youtube.com/watch?v=Ke90Tje7VS0
- Integrating Markdown & making React template code:
https://share.vidyard.com/watch/zfsts6icCEKiKacrbfZeg4?
https://share.vidyard.com/watch/Z2bmZESYg7UdHr61zwD3Jo?


### Platform developer
- Required Packages
- Recommended tools
- Resources to get started
- Docker for beginners: https://www.youtube.com/watch?v=3c-iBn73dDE

### Data Science
- Resources to get started
- https://hrithiks-notes.netlify.app/ai - These are the notes I took for CSI4106 - Intro to AI at uOttawa.
- https://ml-course.github.io/ - This is a course curated with Notes, Sample Code & Projects. The concepts here are pretty abstract, but definitely still helpful in terms of learning the theory. I strongly suggest you go through the Labs as they are more hands on.
- https://pandas.pydata.org/, https://scikit-learn.org/, these are two libraries that will be heavily used, so I would say get familiar with some of the basic concepts and how they are structured.
- https://www.kaggle.com/c/titanic - This is the introductory contest that Kaggle recommends for beginners. And this here: https://github.com/CoderHahs/ml-training/blob/master/Kaggle/Titanic/Notebooks/Titanic-EDA.ipynb, is my solution to this contest.
- https://www.coursera.org/learn/python-data-analysis#syllabus - This is a course I highly recommend doing as it goes over the basics needed for a strong foundation in Data Science
- https://www.coursera.org/learn/python-for-applied-data-science-ai#syllabus - This course is also great, but it is more advanced and again concepts are advanced. I recommend doing this course after you do the course above.