Trending their fandom’s names on Twitter, funding the big screen adaptation of their favorite shows via Kickstarter, and in some cases, getting out on the streets for physical protests—Media fans and fandoms have become more visible in the digital age. However, fan practices pre-date the widespread use of the internet. This course will explore the past and the present of media fandom alongside the ways in which fans have been represented and studied.

Television has long been associated with domestic—both in terms of home and the nation— consumption. However, digital technologies have challenged this confinement. Following the lead of satellite technologies and the global wave of economic liberalization, television content has become more mobile, and spread of digital technologies has further contributed to this mobility. This course examines the global journey of television starting from its conception and ending in the current digital era. (E)

Television has long been associated with domestic—both in terms of home and the nation— consumption. However, digital technologies have challenged this confinement. Following the lead of satellite technologies and the global wave of economic liberalization, television content has become more mobile, and spread of digital technologies has further contributed to this mobility. This course examines the global journey of television starting from its conception and ending in the current digital era. (E)

Same as ENG 291. An intermediate-level workshop in which writers develop their skills through intensive reading, writing, revising, and critique. Topic changes annually. Emphasis on narrative writing, broadly defined to include a variety of genres, depending on the interests of the current holder of the Lakes writing residency. Enrollment limited to 12: This interdisciplinary course explores community-engaged scholarship connecting marginalized communities with academics to jointly address our world's complex social problems.

This seminar will cover topics related to the broad issue of detecting network interference. This will
include topics such as traffic differentiation, surveillance, and blocking of content. Prerequisite: CSC 249. (E)

This course provides an introduction to the principles and practice of network security with a focus on
both fundamentals and practical information. The three key topics of this course are cryptography,
network security, and protecting information technology resources. Subtopics include ciphers, key

Covers advanced data structures and algorithms with an emphasis on object-oriented implementation,
comparative efficiency analysis and applications. A variety of algorithmic approaches will be discussed (divide-and-conquer, dynamic programming, greedy algorithms, graph traversal). We'll go beyond classical searching and sorting to graphs and networks and other applied problems, as well as a selection of NP-hard ones. Prerequisites: CSC 111, CSC 212, MTH 153 (Discrete), MTH 111 (Calculus I) or other math course beyond it.

This one-semester course leverages students’ previous coursework to address a real-world data analysis problem. Students collaborate in teams on projects sponsored by academia, government, and/or industry. Professional skills developed include: ethics, project management, collaborative software development, documentation, and consulting. Regular team meetings, weekly progress reports, interim and final reports, and multiple presentations are required. Open only to majors. Prerequisites: SDS 192, SDS 291 and CSC 111.

In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering.

(Formerly MTH/SDS 291). Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 30.