    # 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:

1. a strong foundational education in mathematics, statistics, computer science, as well as
2. 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, organized by interest area, each of which satisfy the requirements of the major:

One does not need to follow any of these lists to complete the major. Ultimately, any choice of cross-disciplinary electives is allowable under the requirements for the 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
1. MATH 451 - Probability Credits: (3)
2. MATH 452 - Mathematical Statistics Credits: (3)
3. MATH 352 - Statistical Data Analysis Credits: (3)
4. MATH 353 - Advanced Statistical Data Analysis Credits: (3)
5. MATH 455 - Statistical Learning Credits: (3)
6. CSCI 301 - Software Development Credits: (3)
7. CSCI 303 - Algorithms Credits Credits: (3)
8. CSCI 416 - Introduction to Machine Learning Credits: (3)
9. CSCI 421 - Database Systems Credits (3)
10. CSCI 426 - Simulation Credits: (3)
###### Econometrics Sample Course List
1. MATH 451 - Probability Credits: (3)
2. MATH 452 - Mathematical Statistics Credits: (3)
3. MATH 352 - Statistical Data Analysis Credits: (3)
4. MATH 353 - Advanced Statistical Data Analysis Credits: (3)
5. MATH 455 - Statistical Learning Credits: (3)
6. ECON 308 - Econometrics Credits: (3)
7. ECON 380 - Experimental Economics Credits: (3)
8. ECON 407 - Cross Section Econometrics Credits: (3)
9. ECON 408 - Time-Series Econometrics Credits: (3)
10. ECON 414 - Bayesian Econometrics Credits: (3)
###### Mathematical Statistics Sample Course List
1. MATH 451 - Probability Credits: (3)
2. MATH 452 - Mathematical Statistics Credits: (3)
3. MATH 352 - Statistical Data Analysis Credits: (3)
4. MATH 455 - Statistical Learning Credits: (3)
5. MATH 311 - Elementary Analysis Credits: (3)
6. MATH 408 - Advanced Linear Algebra (3)
7. MATH 424 - Operations Research: Stochastic Models Credits: (3)
8. CSCI 303 - Algorithms Credits Credits: (3)
9. CSCI 688 - Linear Regression Credits: (3)
10. CSCI 688 - Design of Experiments Credits: (3)

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
1. MATH 451 - Probability Credits: (3)
2. MATH 452 - Mathematical Statistics Credits: (3)
3. MATH 352 - Statistical Data Analysis Credits: (3)
4. MATH 455 - Statistical Learning Credits: (3)
5. MATH 424 - Operations Research: Stochastic Models Credits: (3)
6. MATH 465 - Mathematics of Financial Economics
7. ECON 308 - Econometrics Credits: (3)
8. ECON 408 - Time-Series Econometrics
9. CSCI 688 - Linear Regression Credits: (3)
10. CSCI 668 - Reliability Theory Credits: (3)

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