From 46b2eb77d5315e429220ea6e8e8bf8b268f17a7d Mon Sep 17 00:00:00 2001
From: vaporwave-bug <35856922+ReshmaM8@users.noreply.github.com>
Date: Mon, 4 Oct 2021 20:57:20 -0400
Subject: [PATCH] create software dev doc

base; added for software, platform & data science.
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 create mode 100644 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.