STST - Statistics Major (BS)
Program Title
Program Type
Degree Designation
Department(s)
Program Description
Effective: Fall 2021
Statistics is the practice of working with data using computational and quantitative techniques: collecting, organizing, modeling, analyzing, summarizing, and visualizing, often in order to make well-founded inferences or predictions from data. Statistical methods and ways of thinking are important in many disciplines, from physical sciences to the humanities; and statistical evidence is crucial for high-stakes decision-making in realms like medicine, public policy, and finance.
The ability to handle and think critically about data, and to understand the design, assumptions, and limitations of statistical analysis and studies fit well within a liberal arts education. Drew’s statistics program has a strong computational, applied, and interdisciplinary focus that prepares students to participate in quantitative research and careers, to apply statistics in their areas of study, and to better understand, improve, and protect the world in which we live.
Requisites
Requirements for the Major (48 credits)
I. Core Courses (32 credits)
course - Topics in Single and Multivariable Calculus OR course - Calculus and Analytic Geometry III
course - Statistical Computing in R
course - Introduction to Statistics
course - Discrete Mathematics
course - Probability
course - Applied Regression Analysis
course - Statistical Theory
course - Statistical Machine Learning
Note: Students who want to apply statistics to other disciplines are advised to take course while those who wish to pursue postgraduate work in statistics or data science are advised to take course, course, and course.
II. Four Intermediate or Upper-Level Courses (16 credits)
Choose 4 from the following courses, at least 3 of which should be from MATH, STAT, DATA, or CSCI:
course - Data Visualization
course - Applied Data Analysis
course - Modeling and Simulation
course - Special Topics in Statistics
course - Intermediate Statistics
course - Sports Statistics
course - Text Mining
course - Bayesian Statistics
course - Databases & Information Management
course - Linear Algebra
At most, one applied course from another discipline that uses statistics with a course prerequisite may be counted in this section, such as: