We've been sharing materials to help emerging data scientists get started with the Call for Code Spot Challenge for Wildfires in slack channels etc. I am summarizing the materials here to make the content more easily accessible. With many thanks to Arvind Betrabet, Stefano Gliozzi, Vinay Kumar, Marco De Ieso, Asad Mehmood, Yamini Rao
Main links for the challenge
For the challenge: Final submission to the leaderboard for the challenge must take place before midnight GMT on February 2, 2021
For learning : You will find the challenge evaluation script here https://github.com/Call-for-Code/Spot-Challenge-Wildfires/tree/main/evaluation_scripts and if you consult the readme you can run the script yourself. With the supplied datasets and readme, the evaluation CSVs you can build models and evaluate yourself.
Self Study Materials to Get Started
In order to predict wildfires in Australia for February 2021 and participate in the challenge, we are providing a list of 7 hours minimum self study to get started if you are not very much involved with data science already.
(1) Data Science Methodology (optional)
(2) Look at the specific wildfires problem (1 hour minimum)
(3) Learn some Python & Jupyter Notebooks (3 hours minimum)
(4) Get started with Jupyter (1 hour minimum)
(5) Applied Data Science with Python at Cognitive Class (optional)
(6) Learn some Scikit Learn (30 minutes minimum)
- https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html (30 minutes - 10 hours or more) - Look at Regression in particular - follow the links in the map
(7) Learn some Time Series for forecasting ( 1 hour minimum)
(8) Hypothesis Testing (30 minutes)
(9) Learn about model building tools to work with the data (optional)
More Suggestions from Marco De Ieso
For those who want to build a career path in Data Science, Marco De Ieso recommends 2 standards courses from Andrew Ng which you will find in the Coursera Platform:
· Machine Learning: https://www.coursera.org/learn/machine-learning?
· Deep Learning Specialization: https://www.coursera.org/specializations/deep-learning?#courses
As a very good reference book, Marco suggests consulting “An Introduction to Statistical Learning” by Trevor Hastie and Rob Tibshirani:
Your feedback on these materials is most welcome