Back

Discover CALS

See how our current work and research is bringing new thinking and new solutions to some of today's biggest challenges.

Information for Fall 2023 or Spring 2024 students

Director of Undergraduate Studies: Joe Guinness

Grades

Courses required for the major must be taken for letter grades. To remain in good standing in the major, a student must have a GPA of at least 2.3 in all courses required for the major including advanced electives. A student must earn a grade of C- or better in every required course; if a student receives a lower grade in a required course, the course can be retaken until a C- or better is earned, or the requirement can be satisfied by another course. If these requirements are not met, a student may, if desired, transfer to the General Studies major but still complete the coursework required for the major. 

Course substitutions

If a student’s faculty advisor approves in advance, the student may substitute a similar course for a requirement of the Biometry and Statistics major. For example, BTRY 4090 may be replaced by another suitable course in the theory of statistics, such as MATH 4720. 

Double majors

A student may fulfill the requirements of two distinct majors. If both majors are in the same college, the double major can be officially recognized. If you wish to do this, you should discuss your situation as early as possible with the Assistant Director of Undergraduate Advising (Julia Aquadro at jra269 [at] cornell.edu) and an advisor in the second major. A faculty advisor in the second major should be arranged.

Transferring credits to the major

It is important to distinguish between transfer credits toward graduation, which are evaluated by your college Registrar’s Office, and transfer credits toward the major, which are evaluated by the statistics faculty. It is the individual student’s responsibility to provide sufficient information to the Registrar’s Office and the statistics faculty for evaluation of transfer credits, including Advanced Placement credits. 

Transferring into the major/Declaring a major

A student must be in good academic standing in the program from which the student is transferring and also by the standards of their college. Please review the admissions requirements at CALS websites for Transfer Applicants and Internal Transfer for more information. If you are switching to the Biometry and Statistics major within CALS, please contact the Assistant Director of Undergraduate Advising (Julia Aquadro at jra269 [at] cornell.edu).

Major Requirements

All required classes must be taken for letter grade, only grades of C- or higher will count towards major requirements. 

Calculus I: MATH 1110

Calculus II: MATH 1120 or MATH 1220 or MATH 1910 

Multivariable Calculus: MATH 2130 or MATH 1920 or MATH 2200 or MATH 2230

Linear Algebra: MATH 2310 or MATH 2940 or MATH 2210 or MATH 2240 

Statistical Methods I: BTRY 3010/STSCI 2200 

  • Equivalents include AEM 2100, BTRY 6010, ENGRD 2700, HADM 2010, ILRST 2100, MATH 1710, PAM 2100/2101, PSYCH 2500, SOC 3010, or STSCI 2150. 
  • Students may have this requirement waived by adequate performance in AP Statistics: for Biometry and Statistics majors, CALS requires a score of 5 in AP Statistics, (see Advanced Placement and Non-Cornell (Transfer) Credit)

Statistical Methods II: BTRY 3020/STSCI 3200 

Probability: BTRY/STSCI 3080

  • Equivalents include MATH 4710, ECON 3130 or ORIE 3500.

Linear Models: BTRY/STSCI 4030 or ECON 3140

Theory of Statistics: BTRY/STSCI 4090 or MATH 4720 

Statistical Computing: STSCI 4520 

Students entering the major as sophomores (including transfers and Arts and Sciences students declaring a major) must have completed Calculus I & II and Statistical Methods I. 

Students entering as juniors must have also completed Statistical Methods II and Multivariable Calculus and Linear Algebra.

ECON 3110 and/or ECON 3120 may be substituted for Probability and Linear Models respectively only by double majors in Economics if taken prior to joining Statistical Science.

Students must complete two courses from the Statistical Methods Electives list below.

Statistical Methods Electives 

BTRY/STSCI 3090: Theory of Interest

BTRY/STSCI/ILRST 3100: Statistical Sampling

STSCI/ORIE 3510: Introduction to Engineering Stochastic Processes I

STSCI/ILRST/INFO 3900: Casual Inference

STSCI/ILRST 4010: Great Ideas in Statistics

STSCI 4060: Python Programming and its Applications in Statistics

BTRY/STSCI/ILRST 4100: Multivariate Analysis

BTRY/STSCI/ILRST 4110: Categorical Data

BTRY/STSCI/ILRST 4140: Applied Design

BTRY/STSCI 4270: Introduction to Survival Analysis

STSCI/ILRST 4550: Applied Time Series Analysis; cross-listed as ORIE 5550

STSCI 4630: Operations Research Tools for Financial Engineering

STSCI 4740: Data Mining and Machine Learning (or CS 4780: Introduction to Machine Learning,

or CS 4786: Machine Learning for Data Science)

STSCI 4780: Bayesian Data Analysis: Principles and Practice

STSCI/ORIE 5640: Statistics for Financial Engineering

BTRY 4820: Statistical Genomics

BIOCB 4840/CS 4775 (previously BTRY 4840): Computational Genetics and Genomics

BIOCB 4380 (previously BTRY 4380): Quantitative Genomics and Genetics

BIOCB 4381 (previously BTRY 4381): Biomedical Data Mining and Modeling

CS 4740: Natural Language Processing

ECON 4110: Cross Section and Panel Econometrics

NTRES 6700: Spatial Statistics

ORIE 4741: Learning with Big Messy Data

 

BTRY 4980, 4990 and STSCI 4970, 4990 are recommended, but cannot be used for major requirements.

The two Statistical Methods Electives must include at least one STSCI course or at least one BTRY course.

Students will take three thematically linked courses covering topics related to statistics. The courses should either have a quantitative component that involves probabilistic reasoning or covers mathematical or computational tools that are used within statistics.

  • Introduction to Computing: A Design and Development Perspective

or

  • CS 1112: Introduction to Computing: An Engineering and Science Perspective

may be used as one of these courses in conjunction with two others in any discipline. This is strongly recommended for anyone without prior programming experience.

Courses from the Statistical Methods list may be used to satisfy this requirement, but no course can be used for both requirements. Unless otherwise noted all courses should be taken at the 3000 level or above.

Potential course sequence suggestions are given at the end of this document.

The following are suggested external elective subjects along with potential courses. This list is not exhaustive and any external elective sequence should be agreed on with your faculty advisor. 

Note that individual courses may not be available some years and specific course offerings may change. 

Mathematical Statistics

(recommended if you are considering graduate school in statistics) 

MATH 3110/4130: Mathematical Analysis and two of 

Any MATH classes at the 3000 level or above 

CS 2110: Object Oriented Programming 

ORIE 3300/3310: Optimization 

ORIE 4580: Simulation Modeling and Analysis 

CS 3220 or CS 4220: Scientific Computing and Numerical Analysis 

Statistical Methods 

Three further courses from the Statistical Methods electives. 

Computational Statistics and Data Management 

STSCI 3040: R Programming for Data Science

STSCI 4060: Python Programming and its Applications in Statistics 

CS 2110: Object Oriented Programming 

CS 3110: Data Structures and Functional Programming 

CS 4320: Introduction to Database Systems 

CS 4786: Machine Learning for Data Science 

INFO 3300: Data Driven Web Applications 

ORIE 3120: Practical Tools for Operations Research, Machine Learning and Data Science

Statistics, Policy and Communication 

COMM 4860: Risk Communication 

ILRST 3130: The Ethics of Data Analysis 

INFO 4200: Information Policy: Research, Analysis and Design 

INFO 4250: Surveillance and Privacy 

INFO 4270: Ethics and Policy in Data Science 

INFO 4700: Data and Algorithms in Public Life 

STS 3020: Science Writing for the Media 

STS 3811/PHIL 3810: Philosophy of Science 

Economics 

ECON 3030: Intermediate Microeconomic Theory 

ECON 3040: Intermediate Macroeconomic Theory 

Any ECON courses at 4000 level or above, but particularly 

ECON 4020: Game Theory 

ECON 4110: Cross Section and Panel Econometrics

ECON 4130: Statistical Decision Theory 

ECON 4220: Financial Economics 

Actuarial Studies 

BTRY/STSCI 3090: Theory of Interest 

BTRY/STSCI 4270: Survival Analysis 

STSCI/ORIE 4550: Applied Time Series Analysis 

AEM 2241: Finance or ECON 4220: Financial Economics 

AEM 4210: Futures, Options and Financial Derivatives or ECON 4220: Financial Economics 

STSCI/ORIE 5640: Statistics for Financial Engineering 

Finance 

AEM 4210: Futures, Options and Financial Derivatives 

ECON 4220: Financial Economics 

ECON 4902: Banks 

HADM 2250: Finance 

STSCI/ORIE 4630: Operations Research Tools for Financial Engineering 

ORIE 4742: Information Theory, Probabilistic Modeling, and Deep Learning, with Scientific and Financial Applications 

ORIE 4820: Spreadsheet-Based Modeling and Data Analysis 

Statistical Genetics 

BIOMG 2800: Genetics

and two of the following:

BIOMG 4870: Human Genetics

BIOCB 4381 (previously BTRY 4381): Biomedical Data Mining and Modeling

BIOCB 4810 (previously BTRY 4810): Population Genetics

BIOCB 4830 (previously BTRY 4830): Quantitative Genetics and Genomics

BIOCB 4840 (previously BTRY 4840): Computational Genetics and Genomics

BTRY 4820: Statistical Genomics

ENTOM 4610: Model-based Phylogenetics and Hypothesis Testing

ENTOM 4700: Ecological Genetics

Information Sciences 

INFO 3300: Data-Driven Web Applications 

INFO 3350: Text Mining History and Literature 

INFO 4154: Analytics-driven Game Design 

INFO 4310: Interactive Information Visualization 

Computer Science and Machine Learning 

CS 2110: Object-Oriented Programming 

CS 3220 or CS 4220: Scientific Computing and Numerical Analysis 

CS 4700: Foundations of Artificial Intelligence 

CS 4740: Natural Language Processing 

CS 4780: Introduction to Machine Learning 

ORIE 4741: Learning with Big Messy Data 

ORIE 4742: Information Theory, Probabilistic Modeling, and Deep Learning, with Scientific and Financial Applications 

ORIE 6741: Bayesian Machine Learning 

Quantitative Biology and Ecology 

BIONB 3300: Computational Neuroscience 

BIONB 4220: Modeling Behavioral Evolution 

ENTOM 4700: Ecological Genetics 

MATH 3610: Dynamic Models in Biology 

NTRES 4110: Quantitative Ecology & Management of Fisheries Resources 

NTRES 4120: Wildlife Population Analysis 

VTPMD 6660: Advanced Methods in Epidemiology