Mathematics & Statistics

Faculty

Chair: Professor Michael Larsen
Professors: George Ashline, Jim Hefferon
Assistant Professors: Amir Barghi, Chris Desjardins, Cornelia Mihaila
Instructor: Barbara O’Donovan

The Department of Mathematics & Statistics offers Bachelor of Science major degrees in Mathematics and in Statistics and minor degrees in Mathematics and in Statistics. In addition, the department administers the program in Data Science, which offers major and minor degrees in Data Science.

Students wishing to double major in Data Science and Mathematics, Data Science and Statistics, or Mathematics and Statistics should consult with their advisor about rules pertaining to double-dipping. Students wishing to minor in Mathematics, Statistics, or Data Science similarly should consult their advisor about rules for overlap of Major and Minor fields of study.

Math Learning Outcomes:

Mathematics majors are expected to acquire competence in calculus, linear algebra, and probability and statistics. Through these fields, students are to develop understanding and skills in mathematical reasoning, logical deduction, data analysis and interpretation, and problem solving at various levels and in various contexts.

Students will communicate effectively broader mathematical reasoning approaches and more specific problem solving steps.

Students will progress from a procedural/computational understanding of mathematics to a broad understanding encompassing logical reasoning, generalization, abstraction, and formal proof. They are to formulate definitions and apply methods of direct and indirect proof.

Students will work with ideas and approaches representing the breadth of mathematical sciences, ranging from continuous to discrete and theoretical to applied.

Students will undertake an exploration of at least one advanced topic of their own choosing. This will require them to carefully read, analyze, and create mathematical arguments and draw on ideas and make connections with previous coursework.

Students will have experience with a variety of technological tools, such as computer algebra systems, visualization software, statistical packages, and computer programming languages.

Statisics Learning Outcomes:

Statistics majors should be able to:

Distinguish types of studies and their limitations and strengths,

Describe a data set including both categorical and quantitative variables to support or refute a statement,

Apply laws of probability to concrete problems,

Perform statistical inference in several circumstances and interpret the results in an applied context,

Use mathematical tools, including calculus and linear algebra, to study probability and mathematical statistics and in the description and development of statistical procedures,

Use a statistical software package for computations with data,

Use a computer for the purpose of simulation in probability and statistical inference, and

Communicate concepts in probability and statistics using both technical and non-technical language.

Data Science Learning Outcomes:

Students will become proficient in the statistical analysis of data and the use of computation tools for data analysis.

Students will apply statistical and computational tools to applied problems, and clearly communicate the results in both written reports and oral presentations.

Students will understand the importance of proper data management, documentation of work to allow reproducibility of results, and how to assess the ethical considerations of a data science project.