Statistics and Data Science

Faculty
Admission to the Major:
The Major

The university-wide Department of Statistics and Data Science offers undergraduate and graduate degrees in Statistical Science (B.A.), Applied Statistics (MPS), and Statistics (M.S./Ph.D.). The Statistical Science undergraduate major, open to students in Arts and Sciences, provides an interdisciplinary academic program in the study of empirical quantitative reasoning in its scientific and social context. The Statistical Science major has been designed to ensure that students have a firm grounding in both the major area as well as substantial depth in a particular applied area.

Website: stat.cornell.edu

Faculty

M. T. Wells, chair; J. Booth, director of graduate studies; J. Abowd, S. Basu, J. Booth, F. Bunea, J. A. Bunge, T. DiCiccio, A. El Alauoi, J. Entner, J. Guinness, Y. Hong, G. Hooker, M.Elizabeth Karns, K. Kato, N. Kiefer, D. Matteson, F. Molinari, Y. Ning, M. Nussbaum, K. Packard, D. Ruppert, M. Smith, M. Wegkamp, M. T. Wells, X. Yang

The Major:

Note: In addition to the major requirements outlined below, all students must meet the college graduation requirements .

Admission to the Major:

Prerequisites to apply for the major include a minimum 2.50 cumulative GPA and grades of C- or higher in the following courses to ensure foundational mathematical, computational and statistical ability:

MATH 1110 - Calculus I
MATH 1120 - Calculus II or
MATH 1220 - [Theoretical Calculus II] or
MATH 1910 - Calculus for Engineers
STSCI 2200 - Biological Statistics I (crosslisted) or
BTRY 6010 - Statistical Methods I (crosslisted) or
Equivalent courses listed on the Statistics and Data Science website.

Requirements for major:

To complete the major, students must take a minimum of 60 credits.

Statistical Theory (7 courses):

MATH 2210 - Linear Algebra or
MATH 2230 - Theoretical Linear Algebra and Calculus or
MATH 2310 - Linear Algebra with Applications or
MATH 2940 - Linear Algebra for Engineers
MATH 2220 - Multivariable Calculus or
MATH 2240 - Theoretical Linear Algebra and Calculus or
MATH 2130 - Calculus III or
MATH 1920 - Multivariable Calculus for Engineers
STSCI 3200 - Biological Statistics II (crosslisted) or
BTRY 6020 - Statistical Methods II
STSCI 3080 - Probability Models and Inference (crosslisted) or
ECON 3130 - Statistics and Probability or
MATH 4710 - Basic Probability
STSCI 4520 - Statistical Computing (crosslisted)
STSCI 4030 - Linear Models with Matrices (crosslisted) or
ECON 3140 - Econometrics
STSCI 4090 - Theory of Statistics (crosslisted) or
MATH 4720 - Statistics

Statistical Methods (2 courses):

Two (2) additional courses from among:

BTRY 4381 - Biomedical Data Mining and Modeling
BTRY 4820 - Statistical Genomics: Coalescent Theory and Human Population Genomics
BTRY 4830 - Quantitative Genomics and Genetics
BTRY 4840 - Computational Genetics and Genomics (crosslisted)
ECON 4110 - [Cross Section and Panel Econometrics]
ORIE 4741 - Learning with Big Messy Data
STSCI 3090 - Theory of Interest (crosslisted)
STSCI 3100 - Statistical Sampling (crosslisted)
STSCI 3510 - Introduction to Engineering Stochastic Processes I (crosslisted)
STSCI 4060 - Python Programming and its Applications in Statistics
STSCI 4110 - Categorical Data (crosslisted)
STSCI 4140 - Applied Design (crosslisted)
STSCI 4270 - [Introduction to Survival Analysis] (crosslisted)
STSCI 4550 - Applied Time Series Analysis (crosslisted)
STSCI 4740 - Data Mining and Machine Learning
STSCI 4780 - Bayesian Data Analysis: Principles and Practice
STSCI 5640 - Statistics for Financial Engineering (crosslisted)

External Electives (3 courses):

Three thematically-linked courses covering related topics 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 Using Python or CS 1112 - Introduction to Computing Using MATLAB 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 on the Statistical Science and Biometry and Statistics websites.