Area of Concentration Requirements

Mathematics (4 semester courses)

  • Calculus 1
  • Calculus 2
  • Linear Algebra
  • Probability 1 & 2 (each is a one-mod course, making it a semester-long course overall)

It is recommended that students planning an AOC in statistics complete the calculus and linear algebra courses by the end of their second year.

Core Courses (3 semester courses)

  • Dealing with Data I
  • Dealing with Data II
  • Linear Models

Continuation Courses (5 semester courses)

The continuation courses can be selected from the list below.

  • Statistical Programming and Data Science with R
  • Categorical Data Analysis
  • Statistical Learning
  • Introduction to Time Series Analysis
  • Statistical Inference
  • Bayesian Statistics
  • Data Visualization and Communication
  • One course that is offered as part of the graduate Data Science program, such as: Data Munging and Exploratory Data Analysis; Data Visualization, Presentation, Reporting, and Reproducible Research; or Optimization and Machine Learning.
  • One 3000 level course or higher taken in another discipline that uses advanced statistical methods or reasoning, with prior approval from the statistics faculty.

Undergraduate Theses

A comprehensive data analysis project that uses various statistical methods, or a project that thoroughly reviews and evaluates a certain statistical methodology. Alternatively, the development and assessment of new statistical methodology.

Joint Disciplinary AOC Requirements

The minimal course work for a joint degree in Statistics:

Mathematics (1.5 semester course)

  • Calculus 1
  • Probability 1 (one-mod course)

Core Courses (3 semester courses)

  • Dealing with Data I
  • Dealing with Data II
  • Linear Models 

Continuation Courses (3 semester courses)

  • Any 3 upper level semester courses offered by statistics faculty, or 2 such courses plus an intermediate statistics course taken outside of Statistics

VIEW STATISTICS ACADEMIC LEARNING COMPACT

Recent courses in Statistics

Dealing with Data I/II
Linear Models
Statistical Programming and Data Science with R
Categorical Data Analysis
Introduction to Time Series Analysis
Data Visualization and Communication
Statistical Consulting
Statistical Learning