DCS 101. Introduction to Algorithms.

Algorithms are step-by-step instructions to solving problems. Increasingly, they are indispensable tools in many aspects of our modern lives. In this course, students examine the workings of many common algorithms ranging in complexity and usage, and implement them with computer programs students write. Topics include sorting, searching, pair matching, and Google’s Page-Rank. Students also consider heuristic approaches to certain problems that do not have proven strategies for solving such as the "traveling salesman" problem, taking inspiration from nature. Finally, the course introduces Machine Learning, generic algorithms capable of solving any problem. No prior knowledge of programming required. Prerequisite(s): MATH 105. New course beginning Winter 2017. Enrollment limited to 30. One-time offering. R. Saha.

INDC 352. Preserving the Vibration: Digitizing the Legacy of Vertamae Smart-Grosvenor.

This course introduces public and digital humanities through the life and work of noted journalist, food anthropologist, and public broadcaster Vertamae Grosvenor. Public humanities is concerned with expanding academic discourse beyond academia and facilitating conversations on topics of humanistic inquiry with the community at large. Digital studies provide a plethora of unconventional ways to engage community in public dialogues for the greater good. Drawing from books, operas, NPR audio segments, interviews, cookbooks, and other artifacts of Grosvenor, students create and curate a digital archive. Themes include Gullah culture, African American migration, foodways, memoir, public memory, and monuments. Leading theories and methods of black feminism, material culture, race, food studies, new media and digital humanities are foregrounded. Cross-listed in African American studies, American cultural studies, digital and computational studies, and women and gender studies. Prerequisite(s): AA/AC 119; AA/HI 243; AAS 100; ACS 100; AC/AV 340; AC/EN 395B; AV/WS 287; INDS 250 or 267; REL 255 or 270; or WGST 100. New course beginning Winter 2017. Enrollment limited to 15. Normally offered every other year. M. Beasley.
Interdisciplinary Programs

This course counts toward the following Interdisciplinary Program(s)

DCS 360. Independent Study.

Students, in consultation with a faculty advisor, individually design and plan a course of study or research not offered in the curriculum. Course work includes a reflective component, evaluation, and completion of an agreed-upon product. Sponsorship by a faculty member in the program/department, a course prospectus, and permission of the chair are required. Students may register for no more than one independent study per semester. Open to first-year students. Normally offered every semester. Staff.

DC/EC 368. Big Data and Economics.

Economics is at the forefront of developing statistical methods for analyzing data collected from uncontrolled sources. Since econometrics addresses challenges in estimation such as sample selection bias and treatment effects identification, the discipline is well-suited for the analysis of large and unsystematically collected datasets. This course introduces statistical (machine) learning methods, which have been developed for analyzing such datasets but which have only recently been implemented in economic research. The course also explores how econometrics and statistical learning methods cross-fertilize and can be used to advance knowledge in the numerous domains where large volumes of data are rapidly accumulating. Prerequisite(s): ECON 255. Enrollment limited to 20. Normally offered every year. N. Tefft.

This course is referenced by the following General Education Concentrations

Short Term Courses

DCS s11. Introduction to Programming for Data Analysis and Visualization.

An introduction to computer programming with a focus on quantitative data analysis and visualization. Primarily using the Python programming language, fundamental programming concepts and high-level tools for data manipulation and visualization are introduced using a variety analysis projects with cross-disciplinary applicability. In addition to writing computer programs, the concepts and methods for effective presentation of data are covered. Students with no prior programming experience, but are interested in quantitative projects, are encouraged to participate. New course beginning Short Term 2017. Open to first-year students. One-time offering. R. Nelson.

DCS s20. Introduction to Computer Programming.

This course introduces students to some of the foundational concepts of computer science. Students analyze problems and implement solutions using languages such as C, Java, Python, PHP or Javascript. Irrespective of the particular programming language used, the goal is to ensure that each student acquires the basic concepts to be prepared to learn any programming language on their own in the future. No prior programming experience is required. Not open to students who have received credit for EXDS s20. Enrollment limited to 20. P. Jayawant.

DCS s21. Build with Pi.

The Raspberry Pi computer has greatly expanded the realm and scope of computing. These ultra-cheap and ultra-portable computers are able to directly interface with devices for a multitude of uses. A community of users has documented and showcased many such novel creations. In this course, students learn about the basic workings of a Raspberry Pi and create their own applications. Applications may include scientific measurements, robotics, home and systems automation, and art. Prerequisite(s): DCS 101 or s20; DC/MA s45T; or DC/EC 368. New course beginning Short Term 2017. Enrollment limited to 20. One-time offering. R. Saha.

DC/MA s45T. Mathematical Image Processing.

Digital image processing is a field essential to many disciplines, including medicine, astronomy, astrophysics, photography, and graphics. It is also an active area of mathematical research with ideas stemming from numerical linear algebra, Fourier analysis, partial differential equations and statistics. This course introduces mathematical methods in digital image processing, including basic image processing tools and techniques with an emphasis on their mathematical foundations. Students implement the theory using MATLAB. Topics may include image compression, image enhancement, edge detection, and image filtering. Students conceive and complete projects—either theoretical or practical—on an aspect of digital image processing. Prerequisite(s): MATH 205. Enrollment limited to 30. K. Ott.

This course is referenced by the following General Education Concentrations