The WiDS Cambridge Datathon Workshop is an annual workshop in addition to the main event, WiDS Cambridge Conference. The workshop aims to provide mentorship and training for those interested in participating in the WiDS Datathon Challenge, and, more generally, anyone with a strong interest in data science.

Preparing for the Workshop

Pedagogical materials addressing the technical background necessary for the Datathon challenge will be delivered through pre-recorded videos and structured notebooks. We’ve prepared instructional materials with a range of different depths. We ask that participants review the materials that are complementary to their backgrounds before coming to the workshop -- depending on your background, you may find that the materials cover concepts with which you are already familiar or you may find that only some of the materials cover concepts that are new to you. The best way to see if you need to brush up on a concept is to take the concept quiz (which follows every topic) or run through the exploratory DeepNote notebooks (which follows every 2-3 topics).

About the WiDS 2025 Datathon

The WiDS Datathon is hosted on Kaggle, an online platform for data science competitions and community activities. In this year’s datathon challenge participants will look at how imaging based measurements of the female brain track with diagnosis of neurological or neuropsychiatric diseases.

Resources for Exploring and Understanding the Data

The following are resources for getting started with Kaggle competitions and with working with this year’s data:

Getting Started with Kaggle (Video), Getting Started with Kaggle Tutorial Blog Post
WiDS Data Science Tutorials
1. WiDS Datathon Skill Building Tutorial Kit by Keiko Kamei
2. Data Science Modules by UC Berkeley Professor
Exploratory Data Analysis (Video)

Join Slack Workspace here: WiDS Cambridge 2025 Datathon

Technical Background for the Datathon Challenge

I. Programming

It would be helpful for participants to have some familiarity in python programming. You can familiarize yourself with python by completing online tutorials, for example: https://www.learnpython.org. In particular, it’s helpful for participants to be able to manipulate data using pandas DataFrames, perform basic operations with numpy Arrays and make use of basic plotting functions (e.g. line chart, histogram, scatter plot, bar chart) from matplotlib:

For this workshop, we will be using DeepNote - a free cloud computing service that comes with pre-installed machine learning tools. Deepnote allows you to easily work on your data science projects, together in real-time and in one place with your friends and colleagues. It allows you to create and share documents that contain live code, equations, visualizations and narrative text. You can familiarize yourself with the interface of DeepNote notebooks by reading the following tutorials (remember you don’t need to install anything!):

Finally, we will be working with random variables in this workshop and will be reasoning about them through their distributions. But we’ll only need a bit of familiarity with these concepts:

Probability distributions in python

Skills Check for Participants

You might find the following Deepnote notebook useful to get a sense of the types of computational tasks that we will be using during the workshop

Introduction to Deepnote Notebook and python Libraries for Data Science

Instructions:

Open the notebook, under the drop-down menu by the name of the project, WiDS Datathon 2024, choose to Duplicate the project.
If you want to share your work with others: under ‘Share’ (upper right-hand side of your screen), change the permissions to “Public access: On” and share the link, or invite specific collaborators.

II. Machine Learning

The materials will be broken down into a sequence of bite-sized concepts. Each concept will be introduced in a short 10-20 minute video; following each video, there will be a short concept-check quiz for the viewer to test their understanding. For each topic, we have selected some supplementary readings that may be helpful. After two or three topics there will be an DeepNote notebook with starter code and experiments to help you further explore these topics.

These materials are taken from DSC6232 Machine Learning and Computational Statistics, an intensive summer data science course run by IACS and the University of Rwanda. You can find the complete set of lecture materials on the course website.

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