The Wharton Financial Analytics initiative (WFA) initiative rests on three pillars:
education, research, and practice.
It is difficult, if not impossible, to imagine a financial application in
which data does not play a central role. Financial education must recognize
and embrace this reality. WFA is re-imagining and modernizing financial education
by placing data analytics at the center of all financial teaching. Students will
develop fluency in working with and interpreting financial data for the purpose
of making financial decisions in personal and profession settings. By integrating
data and analytics with financial theory, students will develop a deeper
understanding of financial principles and be better prepared to engage financial challenges.
Research is the engine providing answers to todays and tomorrow’s questions. WFA
supports empirical research that expands the frontier of financial knowledge by answering
those questions. Specifically, research support will be both financial and nonfinancial,
the latter of which includes aiding scholars with data acquisition, the development of
sharable data warehouses and codebases, the integration of research into the classroom,
and the promotion of Wharton research.
Industry engagement, or “practice,” ensures the relevance of the educational and research
initiatives, as well as providing a conduit for the transfer of knowledge between academia
and industry. Engagement can take many forms including co-teaching, guest lectures, data
sharing, collaboration on educational materials, event sponsorships and participation,
student internships, and pro bono consulting.
WFA views the pillars not as independent functions, but as integrated solutions aimed at
empowering people to make better financial decisions. It is the intersection of education,
research, and industry engagement that will enable WFA to achieve its vision.
If you or your company are interested in WFA, please contact me
via email (mrrobert@wharton.upenn.edu)
to discuss engagement options.
Data Labs
Data labs are case-like exercises in which students are asked to solve busines problems
using the scientific method, and data and analytics.
In each lab, students execute a complete data science worklow
including:
data acquisition,
ingestion and verification,
data cleaning and manipulation,
exploratory data analysis,
statistical modeling/machine learning, and
inference.
Their deliverables consist of annotated Jupyter Notebooks
clearly articulating the problem, hypotheses, empirical evidence,
and plan of action supported by their analysis.
Below are examples of data labs used in the
Data Science for Finance course.
Part of the Data Science for Finance program is a captstone project in which students
undertake an empirical investigation on a topic of their choosing.
The choice of topics is unrestricted, determined entirely by students' interests.
At the end of the semester students are required to deliver all code, a 35-page writeup,
and a slide deck that they present in class.
Below are examples of slide decks presented in past classes.