Data Science

Director: Ellen Seljan
Administrative Coordinator: TBA

Data science is an interdisciplinary field of study dedicated to extracting knowledge from data sets. In a world awash with data, nearly every field of endeavor and inquiry is being transformed by data science. This emerging discipline combines coursework in computing, statistics, and various domains of application spanning the arts, humanities, natural sciences, and social sciences. Our coursework will teach you best practices in data collection, management, measurement, visualization, analysis, and inference. Additionally, in our program, you will not only learn to harness data, but also understand its societal consequences. Training in data science at Lewis & Clark will foster quantitative problem-solving skills and cultivate students as lifelong interdisciplinary learners, capable of tackling wicked problems and exploring for the global good.

The interdisciplinary minor is supervised by a group of faculty from several departments. Student advising is provided by faculty teaching courses in the program.

The minor is designed to guide students in the pursuit of the following learning outcomes:

  • Obtain, process, and transform complex data sets.
  • Develop programming abilities conducive to problem solving in multiple high-level computer programming languages.
  • Build and assess data-based statistical models for both prediction and causal inference.
  • Recognize and analyze ethical issues in data science related to algorithmic bias, artificial intelligence, intellectual property, data security, data integrity, and privacy.
  • Effectively communicate knowledge extracted from data orally, visually, and in written formats.

Minor Requirements

A minimum of 24 semester credits distributed as follows:

  • DSCI 140 Introduction to Data Science
  • CS 171 Computer Science I
  • One introductory statistics course chosen from the list below.
  • One advanced statistics course chosen from the list below.
  • One social impact course chosen from the list below.
  • One elective course chosen from the list below.

Introductory Statistics Courses

Statistics
Calculus & Statistics for Modeling the Life Sciences
Statistical Concepts and Methods
Research Methods in Political Science
Statistics I

Advanced Statistics Courses

Econometrics
Linear Models
Simulation-Based Statistical Methods
Probability and Statistics I
Probability and Statistics II
Statistics II

Social Impact Courses

Social Impact courses are under development; see program director for currently available substitutions.

Data Science Electives

Digital Media II
Digital Media III
Phylogenetic Biology and Molecular Evolution
Computer Science II
Artificial Intelligence and Machine Learning
Algorithm Design and Analysis
Technology, Institutions, and Economic Growth
Global Health Economics
Technologies of the Future
Climate Science
Spatial Problems in Earth System Science
Epidemiology
Discrete Mathematics
Linear Algebra
Linear Models
Simulation-Based Statistical Methods
Probability and Statistics I
Probability and Statistics II
Philosophy of Science
Topics in Physics (if topic is Computational Physics)
Biomedical Imaging
Public Opinion and Survey Research
Policy Analysis
Human-Computer Interaction
Digital Media and Society
Argument and Persuasion in Science
Cyborg Anthropology

At least 12 semester credits must be exclusive to the minor (may not be used in any other set of major or minor requirements).

Faculty

Peter Drake. Associate professor of computer science, chair of the Department of Mathematical Sciences. Artificial intelligence, data science, software development. PhD 2002 Indiana University. MS 1995 Oregon State University. BA 1993 Willamette University.

Joel A. Martinez. Associate professor of philosophy, chair of the Department of Philosophy. Ethical theory, normative ethics, ancient philosophy, logic. PhD 2006 University of Arizona. BA 1997 New Mexico State University.

G. Mitchell Reyes. Professor of rhetoric and media studies, chair of the Department of Rhetoric and Media Studies. Rhetoric, public memory, public discourse, rhetoric of science. PhD 2004, MA 2000 Pennsylvania State University. BS 1997 Willamette University.

Ellen C. Seljan. Associate professor of political science, chair of the Department of Political Science, director of the Data Science program. American politics and public policy. PhD 2010 University of California at San Diego. BA 2004 Drew University.

Courses

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DSCI 140 Introduction to Data Science

Content: Study of knowledge extraction from data with integrated use of statistics, computer science, and scientific reasoning. Students will gain the foundational skills necessary to solve problems with data, learning how to make quantitative predictions and explain phenomena in numerous applications. By the end of the course, students will be able to access and manipulate publicly available datasets; assess the quality, usefulness, and limitations of real-world data; visualize data in multiple formats; conduct statistical analyses to test hypotheses; and draw causal inferences (and debunk spurious inferences). All analysis will be taught scientifically and reproducibly using R programming.
Prerequisites: None.
Usually offered: Annually, spring semester.
Semester credits: 4.