Datathon Workshop
April 27, 2024
10:00am - 2:00pm
In person: IBM Austin
11501 Burnet Rd, Austin, TX 78758
Join us for the inaugural Women in Data Science (WiDS) Austin Datathon Workshop!
Join us for an exciting WiDS datathon workshop for beginner data scientists or those new to datathons. Gain hands-on experience with real data, learn data analysis tips, and explore regression models. Network with fellow data enthusiasts and enjoy a complimentary lunch.
The datathon and workshop are open to all genders.
Datathon 2024 Challenge Theme: Equity in Healthcare
This year’s Datathon focuses on equity in healthcare, with sponsor Gilead Sciences providing rich data on metastatic breast cancer treatment, providers, facilities, and patients. Participants will analyze a unique dataset to identify possible inequities in patient care, such as treatment duration and time to adoption of treatment.
Agenda
10:00-10:30
10:30-11:30
11:30-12:00
12:00-12:45
12:45-1:45
1:45-2:00
Intro to Datathon and Kaggle
Exploratory data analysis using sample Notebooks
Guest speaker: Susheela Singh
Lunch and networking
Regression models using sample Notebooks
Submit results to Kaggle
Prepare
Guest Speaker
Susheela (Patwari) Singh
Staff Data Scientist at YouTube
We are thrilled to welcome Susheela Singh, a Staff Data Scientist at YouTube, as our guest speaker! Susheela will share insights from her professional journey and discuss her current role at YouTube. The session will conclude with an engaging Q&A segment, offering attendees the opportunity to learn from her expertise and gain valuable insights into the field of data science.
Presented by WiDS Austin Ambassadors
Anupama Garani
Senior Data Analyst at PIMCO
Bhakti Saoji
Data Scientist at Hoopla Digital
Keely Wright
Senior Technical Program Manager at IBM
Aurna Mukherjee
Liberal Arts & Science Academy student
Stephanie Mozley
Associate Director at Merck
Vidhi Sapru
Data Science student at UT Austin
WiDS Austin is independently organized by the ambassadors in Austin to be part of the mission to increase participation of women in data science and to feature outstanding women doing outstanding work.