Statistics and Data Science, PhD

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

  • analysis of observational studies;
  • Bayesian inference, bioinformatics;
  • decision theory;
  • game theory;
  • high dimensional inference;
  • information theory;
  • machine learning;
  • model selection;
  • nonparametric function estimation; and
  • time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

View the University’s Academic Rules for PhD Programs.


The total course units required for graduation is 13.

Core Requirements
STAT 9270Bayesian Statistical Theory and Methods1
STAT 9300Probability Theory1
STAT 9310Stochastic Processes1
STAT 9610Statistical Methodology1
STAT 9700Mathematical Statistics1
STAT 9710Introduction to Linear Statistical Models1
STAT 9720Advanced Topics in Mathematical Statistics1
Electives 1
Select six course units6
Total Course Units13

Electives must include suitable courses numbered 9000 and above, when offered.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Sample Plan of Study

First Year
Probability Theory
Statistical Methodology
Mathematical Statistics
Bayesian Statistical Theory and Methods
Stochastic Processes
Introduction to Linear Statistical Models
Qualifying Examination and First Year Paper
Second Year
Advanced Topics in Mathematical Statistics
Two Electives
Three Electives
Second-Year Paper
Third Year
Directed Study Course
Two Electives
Oral Exam/Thesis Proposal
Electives or Directed Study Units
Fourth Year and Beyond
Directed Study and Dissertation Research