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Applied Statistics
As humans have developed cheaper and smaller sensors, web cameras and other data collection devices, the amount of data available to be analyzed and understood has exploded. Statistics is the mathematical science that pertains to the collection, analysis, interpretation, explanation, and presentation of data. Because of its empirical roots and its focus on applications, statistics is typically considered a distinct mathematical science rather than a branch of mathematics.
The Computational and Applied Mathematics and Statistics (CAMS) program offers a B.S. in Computational & Applied Mathematics and Statistics. Undergraduates with an interest in statistics, "big data", and actuarial science can major in Applied Statistics.
CAMS Applied Statistics Track
Mathematics + Statistics + Computer Science + Domain
The flexibility of the CAMS Applied Statistics track means that pursuing this major gives one both:
- a strong foundational education in mathematics, statistics, computer science, as well as
- substantive applied knowledge in an application domain.
The wide range of cross-disciplinary electives available under the major means one can explore many different domains of application. Below, we list four sample course lists within the CAMS Applied Statistics major, organized by interest area:
These lists are for advising purposes only; the lists the official requirements of the CAMS Applied Statistics major. Nonetheless, these lists provide exemplar cases of a CAMS education combining mathematics, statistics, computer science and an application domain.
Data science sample course list
- MATH 451 - Probability
- MATH 452 - Mathematical Statistics
- MATH 352 - Statistical Data Analysis
- MATH 353 - Advanced Statistical Data Analysis
- MATH 455 - Statistical Learning
- CSCI 301 - Software Development
- CSCI 303 - Algorithms Credits
- CSCI 416 - Fundamentals of Artificial Intelligence / Machine Learning
- CSCI 421 - Database Systems
- CSCI 426 - Simulation
Econometrics sample course list
- MATH 451 - Probability
- MATH 452 - Mathematical Statistics
- MATH 352 - Statistical Data Analysis
- MATH 353 - Advanced Statistical Data Analysis
- MATH 455 - Statistical Learning
- ECON 308 - Econometrics
- ECON 380 - Experimental Economics
- ECON 407 - Cross Section Econometrics
- ECON 408 - Time-Series Econometrics
- ECON 414 - Bayesian Econometrics
Mathematical statistics sample course list
- MATH 451 - Probability
- MATH 452 - Mathematical Statistics
- MATH 352 - Statistical Data Analysis
- MATH 455 - Statistical Learning
- MATH 311 - Elementary Analysis
- MATH 408 - Matrix Analysis
- MATH 424 - Operations Research: Stochastic Models
- CSCI 303 - Algorithms
- CSCI 688 - Linear Regression
- CSCI 688 - Design of Experiments
For those considering graduate school in Statistics we also recommend that, time permitting, as many additional 300- and 400-level MATH courses are taken as possible; for example, consider taking some of the following:
- MATH 302 - Ordinary Differential Equations,
- MATH 332 - Graph Theory and its Applications,
- MATH 403 - Intermediate Analysis,
- MATH 405 - Complex Analysis,
- MATH 428 - Functional Analysis.
Actuarial science sample course list
- MATH 451 - Probability
- MATH 452 - Mathematical Statistics
- MATH 352 - Statistical Data Analysis
- MATH 455 - Statistical Learning
- MATH 424 - Operations Research: Stochastic Models
- MATH 465 - Mathematics of Financial Economics
- ECON 308 - Econometrics
- ECON 408 - Time-Series Econometrics
- CSCI 688 - Linear Regression
- CSCI 668 - Reliability Theory
One may also consider taking additional economics and financial math courses, for example:
- MATH 265 - Financial Mathematics
- MATH 410: Fundamentals of Actuarial Mathematics
- MATH 410: Mathematics of Financial Engineering
- ECON 380 - Experimental Economics
- ECON 414 - Bayesian Econometrics
as well as courses in differential equations, for example:
- MATH 302 - Ordinary Differential Equations, and/or
- MATH 442 - Partial Differential Equations