Ryan is a 2019 Wharton and Penn Law JD/MBA grad. He currently works at Citadel doing event investing. Prior to returning to school, he spent two years in Private Equity at Lindsay Goldberg, before which he worked in Investment Banking at Credit Suisse. Ryan is a Toronto native and attended McGill University for undergrad.
"In the past, one could get by on intuition and experience. Times have changed. Today the name of the game is data" writes Steven D. Levitt. Data mining and statistical analysis have suddenly revolutionized many real life aspects like politics, housing, health analytics, criminal justice and sports...
By using data preparation, statistics, predictive modeling and machine learning, data mining tries to resolve many issues within individual sectors and the economy at large.
STAT 471/571/701 (Modern Data Mining) created and taught by Prof. Linda Zhao, a faculty in the statistics department provides a thorough treatment of data mining techniques such as data cleaning, exploratory data analysis, predictive modeling and machine learning. The course is designed to provide an overview of these techniques with prime focus on analyzing real datasets. The students are self-selective in the way they are strong analytically and are mostly enthusiastic with data science. It is well balanced mixture of upper level undergraduates, Wharton MBAs, MS and Ph.D’s campus wise. The students at the end of the semester are required to conduct studies of their choice focusing on tackling real world problems using the techniques learnt.
We wholeheartedly thank our TA group: Arun Kumar Kuchibhotla, Harrison Beard (Head TAs), Chenyang Fang, Nikakhtar Farnik, Arielle Stern, Alex Zhao. We could not have made it without their support. We also proudly acknowledge the support of WACI (Wharton Customer Analytics Initiative).
Organizing Committee: Jeffrey Junhui Cai, Arun Kumar Kuchibhotla, and Linda Zhao.