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

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 

Sample Path to the Statistics AOC

The sample path starts with the introductory courses Dealing with Data 1 & 2, which are non-calculus based, and the Calculus 1 & 2 sequence, which provides the necessary mathematical background for the study of statistics. In year 2, this is followed by courses in  Probability and Linear Algebra, in addition to at least one applied statistics elective. With these backgrounds, students are well prepared to take the core course in Linear Models along with many other elective courses starting in their third year.

4 Year Sample Plan

Year 1Dealing with Data I (LAC 6)ISPDealing with Data II
Calculus 1 (LAC 7)Calculus 2
LAC course 1LAC course 2
Year 2Statistical Programming with RISPCategorical Data Analysis
Probability 1 and 2 (LAC 8)Linear Algebra
LAC course 3LAC course 4
Year 3Linear ModelsISPStatistical Learning
LAC course 5Time Series
Year 4Statistical InferenceThesis Thesis

2 Year Plan (assuming student has had Calculus 1 & 2 and a one-semester introductory statistics course)

Year 1Statistical Programming with RISPDealing with Data II
Categorical Data AnalysisLinear Algebra
Probability 1 and 2
Year 2Linear ModelsISPStatistical Learning
Time SeriesThesisStatistical Inference