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Data Science Boot Camp

May 2024

May 3, 2024


Jun 7, 2024


You are registered for this program.

Registration Deadlines

May 3, 2024


All interested participants. On-time registration.

May 12, 2024


Add/Drop deadline. Only those with prior experience may petition to be added after 05/03/2024.



Launch, Core Program, Boot Camp, Projects, Certificates


The Erdős Institute's signature Data Science Boot Camp has been running since May 2018 thanks to the generous support of our sponsors, members, and partners. Due to its popularity, we now offer our boot camp online three times per year in two different formats: a 1-month long intensive boot camp each May and a semester long version each Spring & Fall.

This is the 1-month long intensive boot camp.

Registration opens up on March 1st, 2024

Organizers and Instructors


The goal of our Data Science Boot Camp is to provide you with the skills and mentorship necessary to produce a portfolio worthy data science/machine learning project while also providing you with valuable career development support and connecting you with potential employers.

Project Examples


Groundwater Forecasting

Riti Bahl, Meredith Sargent, Marcos Ortiz, Chelsea Gary, Anireju Dudun

Screen Shot 2022-06-03 at 11.31.35 AM.png
github URL

Groundwater is a critical source of water human survival. A significant percentage of both drinking and crop irrigation water is drawn from groundwater sources through wells. In the US, overuse of groundwater could have major implications for the future and forecasting groundwater can be useful in understanding its impact. Building on historical data for four wells, together with surface water and weather data, in Spokane, WA, we construct and evaluate machine learning models that forecast groundwater levels in the area.


Correcting Racial Bias in Measurement of Blood Oxygen Saturation

Rohan Myers, Saad Khalid, woojeong kim, Brooks Miner, Jaychandran Padayasi

Screen Shot 2022-06-03 at 11.31.35 AM.png
github URL

Fingertip pulse oximeters are the current standard for estimating blood oxygen saturation without a blood draw, both at home and in healthcare settings. However, pulse oximeters overestimate oxygen saturation, often resulting in ‘hidden hypoxemia’: a patient has hypoxemia (dangerously low oxygen saturation), but the oximeter returns a healthy oxygen value. Unfortunately, oximeter overestimation of oxygen saturation is exacerbated for patients with darker skin tones due to light-based oximeter technology. This results in Black patients experiencing hidden hypoxemia at twice the rate of white patients. By combining pulse oximeter readings (SpO2) with additional patient data, we develop improved methods for estimating arterial blood oxygen saturation (SaO2) and identifying Hidden Hypoxemia. The predictions of our models are more accurate than pulse-oximeter readings alone, and remove the systematic racial inequity inherent in the current medical practice of using oximeter readings alone.

First Steps/Prerequisites

Participants should have a base-level familiarity with Python. Participants should also be familiar with some basic math concepts. Finally, you will also need to have your laptop or desktop computer set up for the course. If you are new to Python, need a quick math refresher, or if you need help setting up your computer, then please follow the link below.

First Steps

Slack Channel: #slack-channel

Program Content


Program Content


Project/Homework Instructions

Project/Team Formation
Project Submission
Projects README


Click on any date for more details

Please check your registration email for program schedule and zoom links.

Project/Homework Deadlines

To access the program content, you must first create an account and member profile and be logged in.

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