Dylan Small

All Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9950 - Dissertation

  Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

• AMCS9990 - Masters Thesis

  For students writing a Master's Thesis to fulfill the program's requirements. All required coursework (8 CUs) must be completed prior to being enrolled.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• BSTA6990 - Lab Rotation

  Student lab rotation.

• MATH4990 - Supervised Study

  Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4920 - Community Data Science

  This course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups of 3-5 on a data science problem of interest to a community organization. This is an Academically Based Community Service (ABCS) course. Prerequisites: The course presumes that students have taken a sequence of introductory statistics courses such as STAT 1010/1020, or 4300/4310 and that they have taken a course that has exposed them to more advanced techniques such as STAT 4220, 4230, 4420, 4710 or 4730. It will be assumed that students have knowledge of a statistical programming language such as R or Python. Classes such as STAT 4050, 4700, 4770 or 4800 would meet this requirement.

• STAT5900 - Causal Inference

  Questions about cause are at the heart of many everyday decisions and public policies. Does eating an egg every day cause people to live longer or shorter or have no effect? Do gun control laws cause more or less murders or have no effect? Causal inference is the subfield of statistics that considers how we should make inferences about such questions. This course will cover the key concepts and methods of causal inference rigorously. Background in probability and statistics; some knowledge of R is recommended.

• STAT5920 - Community Data Science

  This course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups of 3-5 on a data science problem of interest to a community organization. This is an Academically Based Community Service (ABCS) course. Prerequisite: The course presumes that students have taken a sequence of introductory statistics courses such as STAT 1010/1020, or 4300/4310 and that they have taken a course that has exposed them to more advanced techniques such as STAT 4220, 4230, 4420, 4710 or 4730. It will be assumed that students have knowledge of a statistical programming language such as R or Python. Classes such as STAT 4050, 4700, 4770 or 4800 would meet this requirement.

• STAT9210 - Observational Studies

  This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.

• STAT9620 - Adv Methods Applied Stat

  This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.

• STAT9710 - Theory of Statistics

  Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

• STAT9915 - Sem in Adv Appl of Stat

  This seminar-based course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups on a data science problem of interest to a community organization.

• STAT9950 - Dissertation

  Dissertation

• STAT9999 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll.

• IMS Medallion Lecturer, 2022
• Steve Feinberg Memorial Lecture Series in Advanced Analytics Lecture, Carnegie Mellon University, 2018
• Fellow, American Statistical Association, 2013

Knowledge at Wharton

• Sports and Statistics: Correlating Football to Brain Injury, Knowledge at Wharton - 7/21/2017
• Are Your Customers ‘Clumpy’? What Binge-buying Means for Marketers, Knowledge at Wharton - 12/17/2014
• Bound for the Beach? Bring These Books…, Knowledge at Wharton - 6/26/2012

Wharton Stories

• Uniting Great Minds, Wharton’s Stat Bridge MA Program Takes Flight, Wharton Stories - 09/13/2023
• How to Use Big Data to Make Cities Safer, Wharton Stories - 04/09/2018