WiDS Datathon Resources

Utilize these resources and tutorials to help get you started in the WiDS Datathon:

WiDS Datathon Webinar: Lessons Learned + Best Practices with Health Data

This open-to-all webinar on-demand features distinguished panelists that explore the challenges and opportunities of working with healthcare data, and discusses issues around the technology and the clinical aspect of healthcare machine learning. Webinar slides.
Watch the webinar

Tutorial: Explore the Data

Written by Nathaly Alarcon, WiDS La Paz ambassador
The Women in Data Science competition is now available in Kaggle. In this post we provide you with a basic notebook that will allow you to start solving the challenge. To start solving the Datathon, it is necessary to become familiar with the problem, for this, it is necessary to analyze the data. We will use the Google Collaboratory functionalities to create our initial notebook.
Read the tutorial

Tutorials: Survival Prediction using Tensorflow, Ensemble Learning, H2O AutoML, and more

Written by Usha Rengaraju, WiDS Mysore ambassador
Ensemble methods combine several trees base algorithms to construct better predictive performance than a single tree base algorithm. The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model. When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias.
Read the tutorials

Resource: MATLAB Benchmark Code for WiDS Datathon 2020

Written by Neha Goel, Technical Lead for AI/Data Science competitions at MathWorks
MathWorks is excited to support WiDS Datathon 2020 by providing complimentary MATLAB Licenses, tutorials, and getting started resources to each participant. To request your complimentary license, go to the MathWorks site, click the “Request Software” button, and fill out the software request form. 
Get the resource

Deep Dive Discussion: A Data Scientist's Perspective on the WiDS Datathon

Written by Sharada Kalanidhi, Lead Data Scientist at Stanford University 
Working with the WiDS Datathon dataset over the past week has been a thrilling exercise.  This dataset presents an opportunity to learn about interesting and real-world modeling challenges, and is different from other curated datasets in textbooks and classic machine learning exercises.  For that reason, I discuss some of the challenges you may experience around missing data, multicollinearity and linear/ nonlinear approaches. I will also provide resources to help you on these topics.
​Read the discussion