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 (M.P.S.), and Statistics (M.S./Ph.D.). The Statistical Science undergraduate major, open to students in the College of Arts and Sciences, provides an interdisciplinary academic program in the study of empirical quantitative reasoning in its physical science and social science contexts. The Statistical Science major has been designed to ensure that students have both a firm grounding in the major area and substantial depth in a particular area of application.

Website: stat.cornell.edu

Faculty

M.T. Wells, chair; J. Booth, director of graduate studies; T. DiCiccio, director of undergraduate studies; S. Basu, F. Bunea, S. Das, A. El Alaoui, J. Entner, J. Guinness, G. Hooker, M.E. Karns, K. Kato, N. Kiefer, D. Matteson, F. Molinari, Y. Ning, M. Nussbaum, K. Packard, D. Ruppert, M. Smith, Y.S. Wang, M. Wegkamp, X.D. Yang, 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 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:

All coursework for this major is in person, in Ithaca, unless otherwise noted.
All major requirements must be taken for a letter grade.
A grade of C- or better must be earned to meet major requirements.
Core requirements minimum is 40 credits.
Elective requirements minimum is 18 credits.

Statistical Theory (7 courses):

Statistical Methods (2 courses):

Two (2) additional courses from among:

BIOCB 4381 - Biomedical Data Mining and Modeling
BIOCB 4830 - Quantitative Genomics and Genetics
CS 4740 - Natural Language Processing (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 4100 - Multivariate Analysis (crosslisted)
STSCI 4110 - Categorical Data (crosslisted)
STSCI 4140 - Applied Design (crosslisted)
STSCI 4270 - Introduction to Survival Analysis and Loss Models (crosslisted)
STSCI 4550 - Applied Time Series Analysis (crosslisted)
STSCI 4600 - Statistics for Risk Modeling
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 5640 - Statistics for Financial Engineering (crosslisted)
The two Statistical Methods Electives must include at least one STSCI course or at least one BTRY course.

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: 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 on the Statistical Science website.