Upcoming Events
Beyond Business: Using Data to Protect and Serve
LinkedIn Live
Women in Data Science @ Penn
Perry World House
Philadelphia
Beyond Business
NOVEMBER 15, 2022
12:00 – 1:00 p.m. EST
The Analytics of Finance
How Data Can Help Govern and Grow the Economy
More than ever, analytics is influencing critical decision-making in the field of finance through machine learning, big data, and algorithms. Learn how the financial landscape will evolve with these trends, and what obstacles — and opportunities — to expect.
DECEMBER 14, 2022
11:30 a.m. – 12:30 p.m. EST
Using Data to Protect and Serve
How Analytics Can Drive Social Good
When faced with challenging societal issues such as police reform, human trafficking, and media transparency, analytics can offer surprising ways to increase accountability and drive social good. Discover how data can lead us to more effective solutions.
JANUARY 19, 2023
12:30 – 1:30 p.m. EST
More Than a Game
How Analytics Gives Sports Teams a Competitive Advantage
Sports analytics has taken the spotlight as a driver of competitive advantage for professional teams, elevating the value of data both on and off the field. Explore how the sports industry can embrace data science in order to create dynasties.
Registration coming soon.
Previous Events
WOMEN IN DATA SCIENCE @ PENN
The Wharton School and Penn Engineering were proud to host the third annual Women in Data Science (WiDS) @ Penn Conference on February 9-10, 2022. Over the course of two days, attendees tuned in for talks showcasing the latest advances in data science, live speaker Q&A sessions, and networking opportunities.
This year’s theme – This is What a Data Scientist Looks Like – emphasized the diversity of data science, both in subject matter and personnel. A celebrated interdisciplinary event, WiDS @ Penn welcomed academic, industry, and student speakers from across the data science landscape.
Analytics at Wharton Presents: Timnit Gebru, PhD
On January 24, 2020, Timnit Gebru spoke at the Wharton School on “Consent, Power, Inclusivity, Transparency, Ethics, and Privacy in Collecting Sociocultural Data in Machine Learning: Lessons from Historical Archives.”