Statistics and Data Science, B.S.

Objectives

This degree program is designed for students who want an interdisciplinary course of study that combines computing with statistical analysis and data science. The degree is designed to meet the needs of students who expect to work in a data related field in industry or pursue a graduate degree in either statistics or data science. 

Learning Outcomes

1. Students formulate and/or address questions about univariate data, collect/consider univariate data, analyze univariate data, and interpret results.
2. Students understand, calculate, and interpret descriptive measures  for quantitative and/or categorical variables to describe characteristics of the data.
3. Students create and interpret basic data visualizations for quantitative and categorical variables.
4. Students understand, carry out, and interpret basic inferential statistical procedures for one or two samples.
5. Students understand, carry out, and interpret results from estimating statistical models for bivariate data (e.g., linear regression, interpolation, extrapolation, predictive inference).
6. Students carry out and communicate results from extensive data-driven project(s) related to a real-life problem (extensive means that a single project takes several weeks to complete or a series of projects take more than several weeks to complete and are worth a significant portion of the final grade.
7. Students communicate their analyses and the interpretations of their results in a manner that is appropriate to their discipline in the context of the data (e.g., communication could be emphasized with presentations, oral explanations of results, oral/written answers for in-class work, written explanation of results).
8. Students understand the implications of study design, consider ethics in data collection, can select appropriate statistical methods for data analysis, and can explain the limitations of their analyses and interpretations.
9. Students become critical consumers of statistically-based results reported in popular media, recognizing whether reported results reasonably follow from the study and analysis conducted.
10. Students formulate and/or address questions about multivariate data, collect/consider multivariate data, analyze multivariate data, and interpret results.
11. Students use current statistical software or statistical packages that are appropriate to the discipline and context beyond basic Excel or a calculator.
12. Students write a program (using a programming language) to organize, manipulate, and analyze and extract information from the data.
13. Students study at least one type of advanced data-analytic methods such as (but not limited to): generalized linear models, Bayesian analysis, advanced probability theory and stochastic processes, non-linear models, machine learning, advanced study-design, big data analysis, econometrics, or statistical computing. 

General Major Requirements

Students must complete the corresponding Bachelor of Science University Core requirements as defined by the Frank R. Seaver College of Science and Engineering; students will choose the proper sequence of University Core courses in consultation with their advisor.

Statistics and Data Science majors and minors are not permitted to enroll in a mathematics course without a minimum grade of C (2.0) in that course’s prerequisite. A minimum grade of C (2.0) is required in each course in the lower division major requirements. A minimum cumulative grade point average of C (2.0) is required in the upper division major requirements for graduation.

Bachelor of Science in Statistics and Data Science

(124 S.H.)