Prerequisites to apply for the major include a minimum 2.50 cumulative GPA over at least two (2) semesters at Cornell University; and grades of C or higher in at least three (3) of the following courses to ensure foundational mathematical and statistical ability:

Calculus I and II (MATH 1110 & 1120)

Biological Statistics I (STSCI 2200/BTRY 3010) or equivalent

See below for a list of core requirements, which includes core requirements, suggested electives, and general information about the Biometry and Statistics/Statistical Science major.

To affiliate with the major in Statistical Science, visit our How to Affiliate page. This major is open only to students in the College of Arts & Sciences.

## Students Graduating in or after May 2028

### Required Courses

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

**Statistical Methods I: **BTRY 3010/STSCI 2200

Equivalents include AEM 2100, BTRY 6010, ENGRD 2700, HADM 2010, STSCI/ILRST 2100, MATH 1710, PUBPOL 2100/2101, PSYCH 2500, SOC 3010, and STSCI 2150.

Students may have this requirement waived by adequate performance in AP Statistics:

- Statistical Science majors, Arts & Sciences requires a score of 4 or 5 in AP Statistics, (see https://math.cornell.edu/introductory-stats).

**Introduction to Computing:** CS 1110 or CS 1112

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

**Statistical Methods II: **BTRY 3020/STSCI 3200

**Probability:** BTRY/STSCI 3080 or MATH 4710

Alternatives include ECON 3130 and ORIE 3500

**Linear Models:** BTRY/STSCI 4030

Alternatives include ECON 3140

**Theory of Statistics:** BTRY/STSCI 4090 or MATH 4720

**Statistical Computing: **STSCI 4520

### Elective Courses:

Students must complete *five (5) elective courses* with significant statistical content. Each course must be worth at least 3 credits. A list of pre-approved elective courses is provided below.

*At least three (3) of the five (5) electives must be STSCI/BTRY courses*, or cross-listed with STSCI/BTRY courses.

*Any BTRY/STSCI course numbered 4000 or above (except STSCI 4970)

STSCI 3740: Data Mining and Machine Learning

STSCI 3900: Causal Inference

BTRY/STSCI 3090: Theory of Interest

BTRY/STSCI/ILRST 3100: Statistical Sampling

BIOCB 4381: Biomedical Data Mining and Modeling

BIOCB 4830: Quantitative Genetics and Genomics

BIOCB 4840: Computational Genetics and Genomics

BIOCB 4910: Advanced Population Genetics

MATH 4260: Numerical Analysis; Linear and Nonlinear Problems

MATH 4740: Stochastic Processes

ECON 4110: Cross Section and Panel Econometrics

ECON 4130: Statistical Decision Theory

NTRES 6700: Spatial Statistics

INFO 3300: Data Driven Web Applications

INFO 4310: Interactive Information Visualization

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

ORIE 3300/3310: Optimization

ORIE/STSCI 3510: Introduction to Engineering Stochastic Processes I

ORIE 4580: Simulation Modeling and Analysis

ORIE 4741: Learning with Big Messy Data

ORIE 4742: Info. Theory, Prob. Modeling, and Deep Learning, with Scientific and Financial Applications

CS 3220 or CS 4220: Scientific Computing and Numerical Analysis

CS 3780: Intro to Machine Learning

CS 4320: Introduction to Database Systems

CS 4740: Natural Language Processing

CS 4786: Machine Learning for Data Science

CS 4787: Principles of Large-Scale Machine Learning Systems

CS 4789: Introduction to Reinforcement Learning

### Data Science Track:

Students who opt to follow the Data Science Track will replace the multivariable calculus requirement with CS 2110 (Object Oriented Programming and Data Structures), and use CS 4320 (Introduction to Database Systems) as one of their non-STSCI/BTRY electives. Please note that while it will not be a requirement for the major, multivariable calculus is a prerequisite for some BTRY/STSCI major requirements.

### Double Majors with Economics:

Starting with May 2028 graduates, ECON 3110 and/or ECON 3120 CANNOT be substituted for Probability and Linear Models major requirements. Please see the approved courses for these major requirements above.

## Students Graduating in or before December 2027

### Required Courses:

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

**Statistical Methods I:** BTRY 3010/STSCI 2200

Equivalents include AEM 2100, BTRY 6010, ENGRD 2700, HADM 2010, STSCI/ILRST 2100, MATH 1710, PUBPOL 2100/2101, PSYCH 2500, SOC 3010, and STSCI 2150.

**Students may have this requirement waived by adequate performance in AP Statistics:**

- Statistical Science majors, Arts & Sciences requires a score of 4 or 5 in AP Statistics, (see https://math.cornell.edu/introductory-stats).

**Multivariable Calculus:** MATH 1920 or MATH 2220 or MATH 2230 or MATH 2130

- MATH 2130 will not be offered in the 2024-25 academic year.

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

**Statistical Methods II:** BTRY 3020/STSCI 3200

**Probability: **BTRY/STSCI 3080 or MATH 4710

Alternatives include ECON 3130 and ORIE 3500

**Linear Models:** BTRY/STSCI 4030

Alternatives include ECON 3140

**Theory of Statistics: **BTRY/STSCI 4090 or MATH 4720

**Statistical Computing:** STSCI 4520

### Elective Courses:

Students must additionally complete two Statistical Methods Electives, and another three External Electives that make a thematically linked sequence. Electives, or alternatives to the elective list, will be approved by faculty advisors.

#### Statistical Methods Electives (select two)

BTRY/STSCI 3090: Theory of Interest

BTRY/STSCI/ILRST 3100: Statistical Sampling

STSCI/ORIE 3510: Introduction to Engineering Stochastic Processes I

STSCI 3740: Data Mining and Machine Learning (or CS 3780: Introduction to Machine Learning,

or CS 4786: Machine Learning for Data Science)

STSCI/ILRST/INFO 3900: Casual Inference

STSCI/ILRST 4010: Great Ideas in Statistics

STSCI 4050: Modern Regression Models for Data Science

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 4780: Bayesian Data Analysis: Principles and Practice

STSCI/ORIE 5640: Statistics for Financial Engineering

BIOCB 4381: Biomedical Data Mining and Modeling

BIOCB 4820: Statistical Genomics

BIOCB 4830: Quantitative Genomics and Genetics

BIOCB 4840/CS 4775: Computational Genetics and Genomics

CS 4740: Natural Language Processing

ECON 4110: Cross Section and Panel Econometrics

NTRES 6700: Spatial Statistics

ORIE 4741: Learning with Big Messy Data

STSCI 3600 (2 cr) and STSCI 4610 (2 cr) can be taken together as ONE Statistical Methods elective

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.*

### External Electives (select three):

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.

- CS 1110: 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. Example sequences are provided below, and on the Statistical Science and Biometry and Statistics websites.

### Suggested External Electives

The following are suggested external elective subjects along with potential courses. This list is not exhaustive and external elective sequences/courses should be ** discussed 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 class 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 list.

*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: Biomedical Data Mining and Modeling

BIOCB 4810: Population Genetics

BIOCB 4820: Statistical Genomics

BIOCB 4830: Quantitative Genomics and Genetics

BIOCB 4840: Computational Genetics and 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 3700: Foundations of AI Reasoning and Decision-Making

CS 3780: Introduction to Machine Learning

CS 4740: Natural Language Processing

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

** Double Majors with Economics: **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.

* New Major Requirements:* Students may opt to use the new major requirements (for May 2028 graduates or after) instead of the ones listed above. If students opt into the new requirements, they must inform their faculty advisor and the Assistant Director of Undergraduate Advising about their choice.

### General Information for all Students

**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 major advisor approves in advance, the student may substitute a similar course for a requirement of the Biometry and Statistics or Statistical Science major (including electives).

**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 Director of Undergraduate Studies or Undergraduate Advising Coordinator of the home department of 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 admitting college’s Registrar’s Office, and transfer credits toward the major, which are evaluated by the statistics faculty. For course equivalency within the major, please contact the Assistant Director of Undergraduate Advising. It is the individual student’s responsibility to provide sufficient information to the Registrar’s Office and the Department of Statistics and Data Science for evaluation of transfer credits, including Advanced Placement credits.

**Transferring into the major:** A student must be in good academic standing in the program from which the student is transferring and by the standards of their college. Transfer students will be exempted from all required courses for which they have taken equivalent courses at other colleges or universities (after the course equivalency review process).

- To transfer in as a junior, students must have also completed Calculus II, Statistical Methods II, Multivariable Calculus and Linear Algebra.

**Affiliating with the major:** Students must complete Calculus I, Calculus II and Statistical Methods I before affiliating with Statistical Science in the College of Arts & Sciences, with grades of C or higher.

*Updated June 24, 2024*