## ENROLL Course Search

**NOTE:** There are some inconsistencies in the course listing data - ITS is looking into the cause.

**Alternatives:** For requirement lists, please refer to the current catalog. For up-to-the-minute enrollment information, use the "Search for Classes" option in The Hub. If you have any other questions, please email registrar@carleton.edu.

Your search for courses for 21/FA and with code: STATELEC found 4 courses.

### CS 362.00 Computational Biology 6 credits

Open: Size: 34, Registered: 22, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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Recent advances in high-throughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics.

*Prerequisite:* Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

### MATH 271.00 Computational Mathematics 6 credits

Open: Size: 25, Registered: 17, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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An introduction to mathematical ideas from numerical approximation, scientific computing, and/or data analysis. Topics will be selected from numerical linear algebra, numerical analysis, and optimization. Theory, implementation, and application of computational methods will be emphasized.

*Prerequisite:* Mathematics 232

Not open to students who have already received credit for Mathematics 295 Numerical Analysis

### STAT 220.00 Introduction to Data Science 6 credits

Open: Size: 30, Registered: 25, Waitlist: 0

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8:30am9:40am | 8:30am9:40am | 8:30am9:30am |

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(Formerly Mathematics 285) This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.

*Prerequisite:* Statistics 120 (formerly Mathematics 215), Statistics 230 (formerly Mathematics 245) or Statistics 250 (formerly Mathematics 275)

Formerly Mathematics 285

### STAT 340.00 Bayesian Statistics 6 credits

Closed: Size: 20, Registered: 25, Waitlist: 0

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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

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Formerly MATH 315) An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

*Prerequisite:* Statistics 250 (formerly Mathematics 275)

Fomerly Mathematics 315

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