Catalog

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Digital and Computational Studies

Data and computers are transforming virtually every facet of our professional and personal lives. Increasingly, they are the dominant media for how we generate, apply, and share knowledge. The digital and computational studies program endeavors to prepare students for lives of work and study that require proficiency in using constructed electronic platforms, software, and large, complex data sets. The program is also deliberately problem-oriented and reflective: Instructors in the program assume that by paying attention to the values and motivations underlying the development and use of computers – and the consequences of computers and computation for society – students are more likely to understand what goes on beyond the user interface.

The faculty has established a new interdisciplinary program in digital and computational studies beginning in 2015-16. The program's goals are to advance learning and scholarship across multiple disciplines informed by concepts, methods, and tools of computer science and digital studies. Specifically the program aims to interrogate the values and assumptions of a digitized world; increase understanding of the power and limitations of computers in solving problems; advance understanding of the theory and logic of computation; promote proficiency in the assessment, analysis, and visualization of data; build competency in the analysis of complex relationships among data sources; promote creative and competent use of algorithms in problem solving; and foster connections across disciplines.

The Committee on Digital and Computational Studies is developing the curriculum in 2015-16 with new courses added over the course of this and future academic years. As extant courses are cross-listed in digital and computational studies (DS) and new courses are developed in the program (DCS), they will be listed below.

Courses

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 will cross-fertilize and be used to advance knowledge in the numerous domains where large volumes of data are rapidly accumulating. Prerequisite(s): ECON 255. New course beginning Winter 2016. Enrollment limited to 20. Normally offered every year. N. Tefft.
Concentrations

This course is referenced by the following General Education Concentrations

Learning and Teaching (C084)

Short Term Courses

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.