Data Science

Director: Ellen Seljan
Administrative Coordinator: Sara Asberry

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
Biostatistics in Public Health
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

Data, Privacy, and Ethics
Data and Democracy

Data Science Electives

Digital Media II
Digital Media III
Phylogenetic Biology and Molecular Evolution
Computer Science II
Databases
Artificial Intelligence and Machine Learning
Algorithm Design and Analysis
Technology, Institutions, and Economic Growth
Global Health Economics
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
Science, Technology, and Society
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

Cliff T. Bekar. Associate professor of economics. Economic history, industrial organization, game theory. PhD 2000, MA 1992, BA 1990 Simon Fraser University.

Greta J. Binford. Professor of biology. Invertebrate zoology, biodiversity, evolution of spider venoms. PhD 2000 University of Arizona. MS 1993 University of Utah. BA 1990 Miami University.

Moriah Bellenger Bostian. Associate professor of economics, chair of the Department of Economics. Environmental and resource economics, econometrics. PhD 2010 Oregon State University. MS 2005 Auburn University. BS 2003 Florida State University.

Yung-Pin Chen. Professor of statistics. Statistics, sequential designs. Probability, stochastic processes. PhD 1994 Purdue University. BS 1984 National Chengchi University, Taiwan.

Brian Detweiler-Bedell. Professor of psychology, director of the Bates Center for Entrepreneurship and Leadership. Social psychology, statistics. PhD 2001, MPhil 2000, MS 1998 Yale University. MA 1995, BA 1994 Stanford University.

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

Jeffrey S. Ely. Associate professor of computer science. Computer graphics, numerical analysis. PhD 1990, MS 1981, BS 1976 Ohio State University.

Benjamin Gaskins. Associate professor of political science. American politics, public opinion, media and politics, religion and politics. PhD 2011, MS 2008 Florida State University. BA 2006 Furman University.

Jessica M. Kleiss. Associate professor of environmental studies. Oceanography, interface between the atmosphere and the ocean. PhD 2009 Scripps Institution of Oceanography, University of California at San Diego. BS 2000 Massachusetts Institute of Technology.

Jens Mache. Professor of computer science. Parallel and distributed systems, computer networks, cybersecurity. PhD 1999 University of Oregon. MS 1994 Southern Oregon University. Vordiplom 1992 Universitaet Karlsruhe.

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.

Aine Seitz McCarthy. Assistant professor of economics. Applied microeconomics, development economics, labor and demography, economics of education. PhD 2016 University of Minnesota. BA 2006 Colby College.

Erik L. Nilsen. Associate professor of psychology, chair of the Department of Psychology (Spring). Cognition, methodology, human-computer interaction. PhD 1991, MA 1986 University of Michigan. BA 1984 Graceland College.

Jay Odenbaugh. Professor of philosophy. Ethics, philosophy and the environment, philosophy of science, metaphysics, logic. PhD 2001 University of Calgary. MA 1996 Southern Illinois University at Carbondale. BA 1994 Belmont University.

G. Mitchell Reyes. Professor 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, director of the Data Science program. American politics and public policy. PhD 2010 University of California at San Diego. BA 2004 Drew University.

Elizabeth A. Stanhope. Professor of mathematics, chair of the Department of Mathematical Sciences. Differential geometry, spectral geometry. PhD 2002, AM 1999 Dartmouth College. BA 1995 Carleton College.

Iva Stavrov. Professor of mathematics. Differential geometry, algebraic topology. PhD 2003, MS 2001 University of Oregon. BS 1998 University of Belgrade.

Todd Watson. Associate professor of psychology, co-director of the neuroscience program. Cognitive neuroscience, brain and behavior, statistics. PhD 2005 State University of New York at Stony Brook. MA 2000 Radford University. BS 1997 Pennsylvania State 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.

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DSCI 245 Applied Data Science Practicum

Content: Academic and experiential bridge between classroom theory and real-world application in the domain of data science. Students enrolled in this course will work eight to 10 hours per week in small teams to solve problems and extract value from data. The problems and data sets will vary by year and course offering, but will universally provide opportunities to students to frame research questions, manage and clean data, execute analyses, and communicate results.
Prerequisites: None.
Usually offered: Annually.
Semester credits: 2.