Data Science
Data science is an interdisciplinary field of study focused on extracting insight from data. In a world awash with data, nearly every field of endeavor and inquiry is being transformed by data science. This emerging discipline integrates coursework in computing, statistics, and applied domains across the arts, humanities, natural sciences, and social sciences. Our curriculum emphasizes best practices in data collection, management, measurement, visualization, analysis, and inference. Students also develop an understanding of the ethical, social, and institutional contexts in which data are produced and used.
Courses in this interdisciplinary program are offered by faculty across many academic departments. A faculty-led steering committee oversees program administration and student advising.
The program is designed to guide students toward the following learning outcomes:
- Obtain, process, and transform complex data sets.
- Develop programming skills for problem-solving across multiple high-level programming languages.
- Build and evaluate statistical models for prediction and causal inference.
- Identify and analyze ethical issues in data science, including algorithmic bias, artificial intelligence, intellectual property, data security, data integrity, and privacy.
- Communicate insights derived from data effectively in written, visual, and oral forms.
Major Requirements
A minimum of 44 semester credits, distributed as follows:
- DSCI 140 Introduction to Data Science
- MATH 131 Calculus I, MATH 132 Calculus II, or CS 230 Computational Mathematics*
- CS 171 Computer Science I or CS 172 Computer Science II
- DSCI 250 Intermediate Data Science
- One introductory statistics course chosen from the list below.
- One advanced statistics course chosen from the list below.
- One data ethics or social Impact course chosen from the list below.
- Four credits of internship, practicum or applied learning chosen from the list below.
- One data-intensive course chosen from the list below.
- Thesis or Capstone course: DSCI 400 or DSCI 440.
- Four credits of electives chosen from the general electives list below.
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 general electives list below. May not duplicate courses used to satisfy other requirements.
At least 12 semester credits must be exclusive to the minor (may not be used in any other set of major or minor requirements).
Introductory Statistics
| Statistics | ||
| Biostatistics in Public Health | ||
MATH 105 Statistics | ||
| Statistical Concepts and Methods | ||
| Research Methods in Political Science | ||
| Statistics I | ||
Advanced Statistics
| Applied Statistical Modeling and Inference | ||
| Econometrics | ||
| Linear Models | ||
| Simulation-Based Statistical Methods | ||
| Probability and Statistics I | ||
| Probability and Statistics II | ||
| Statistics II |
Data Ethics or Social Impact
| Data, Privacy, and Ethics | ||
| Data and Democracy | ||
| Digital Media and Society |
Internship, Practicum or Applied Learning
| Cybersecurity Clinics | ||
| Creating Data Dashboards (2 credits) | ||
| Data for Good | ||
| Practicum/Internship | ||
| Applied Data Science Practicum (2 credits) | ||
| Independent Study in Data Science |
Data Intensive
| Databases | ||
| Artificial Intelligence and Machine Learning | ||
| Algorithm Design and Analysis | ||
| Creating Data Dashboards | ||
| Data for Good | ||
| Applied Statistical Modeling and Inference | ||
| Business and Economic Forecasting | ||
| Econometrics | ||
| Introduction to GIS and Mapping | ||
| Landscape Mapping and Monitoring | ||
| Spatial Problems in Earth System Science | ||
| Linear Models | ||
| Simulation-Based Statistical Methods | ||
| Probability and Statistics I | ||
| Probability and Statistics II | ||
| Experimental Methods in the Physical Sciences | ||
| Policy Evaluation | ||
| Statistics II |
Data Science General Electives
Biology
| Disease Ecology | ||
| Phylogenetic Biology and Molecular Evolution |
Computer Science
| Computer Science II | ||
| Computer and Network Security | ||
| Databases | ||
| Cybersecurity Clinics | ||
| Artificial Intelligence and Machine Learning | ||
| Algorithm Design and Analysis | ||
| Advanced Cybersecurity | ||
| Neural Networks for Artificial Intelligence |
Data Science
| Visualizing Berlin | ||
| Creating Data Dashboards ( 2 credits) | ||
| Data for Good | ||
| Practicum/Internship (only with departmental approval) | ||
| Applied Data Science Practicum | ||
| Intermediate Data Science (elective for the minor only) | ||
| Independent Study in Data Science (elective for the minor only) | ||
| Applied Statistical Modeling and Inference | ||
| Data Science Capstone (elective for minor only) |
Economics
| Business and Economic Forecasting | ||
| Technology, Institutions, and Economic Growth | ||
| Econometrics | ||
| Global Health Economics |
Environmental Studies
| Introduction to GIS and Mapping | ||
| Landscape Mapping and Monitoring |
Earth System Science
| Climate Science | ||
| The Fundamentals of Hydrology | ||
| Spatial Problems in Earth System Science |
Health Studies
| Epidemiology |
Mathematics
| Linear Algebra | ||
| Linear Models | ||
| Simulation-Based Statistical Methods | ||
| Probability and Statistics I | ||
| Probability and Statistics II |
Philosophy
| Data, Privacy, and Ethics | ||
| Philosophy of Science |
Physics
| Experimental Methods in the Physical Sciences | ||
| Computational Physics |
Political Science
| Public Opinion and Survey Research | ||
| Policy Evaluation |
Psychology
| Statistics II | ||
| Human-Computer Interaction |
Rhetoric and Media Studies
| Data and Democracy | ||
| Interpersonal Media | ||
| Digital Media and Society | ||
| Science, Technology, and Society |
*This requirement, and associated credits, may be waived by the chair of the Data Science major for students eligible to begin math sequences in higher level courses.
Honors
Students are eligible for consideration for honors upon achieving a minimum 3.50 grade point average in the major at the time of graduation. Honors will be awarded at graduation to eligible students whose capstone project or thesis is judged by the department faculty to demonstrate superior quality, originality, and intellectual insight.
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. Professor of economics and department chair. 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. Social psychology, leadership, judgment and decision making, health psychology, and 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.
Heidi Liere. Assistant professor of biology. Insect Community Ecology, Urban Ecology and Agroecology. PhD 2011 University of Michigan-Ann Arbor. BS 2001 Universidad del Valle de Guatemala.
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. Ethical Theory, Normative Ethics, Ancient Philosophy, Logic. PhD 2006 University of Arizona. BA 1997 New Mexico State University.
Aine Seitz McCarthy. Associate 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. Cognition, methodology, human-computer interaction. PhD 1991, MA 1986 University of Michigan. BA 1984 Graceland College.
Jay Odenbaugh. James F. Miller Professor of Humanities. 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. Professor of political science. American politics and public policy. PhD 2010 University of California at San Diego. BA 2004 Drew University.
Elizabeth A. Stanhope. Professor of mathematics. Differential geometry, spectral geometry. PhD 2002, AM 1999 Dartmouth College. BA 1995 Carleton College.
Todd Watson. Associate professor of psychology. 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
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. 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 or Python programming.
Prerequisites: None.
Usually offered: Annually, fall and spring semester.
Semester credits: 4.
DSCI 210 Visualizing Berlin
Content: Hands-on data science practice in Berlin. Exploration of the city through the lens of data: collecting and analyzing photographs, sound recordings, textual sources, geospatial traces. Students will develop technical fluency in data wrangling, computational analysis, and visual communication through analysis and visualization in R. Throughout the semester, the class will engage in field trips to collect original data. Course is organized by data modality: image, geospatial, audio, and text. Culminates in two student-designed projects: a longitudinal study based on daily data collection over time, and a focused, single-instance field study.
Prerequisites: Acceptance to Germany: Berlin overseas program required.
Usually offered: Alternate Years, fall semester.
Semester credits: 4.
DSCI 211 Creating Data Dashboards
Content: Focus on data dashboards to convey data-driven conclusions. Best practices when planning and constructing a data dashboard; how to construct and publish dashboards using a variety of platforms: R programming, Tableau, ArcGIS Online.
Prerequisites: DSCI 140 or BIO 110 (or any class with R programming) recommended.
Usually offered: Annually, spring semester.
Semester credits: 2.
DSCI 240 Data for Good
Content: Exploration of data science as applied to social, ethical, and moral questions. Through the lens of R programming, students will learn how data science can inform policy decisions, enhance journalism, combat misinformation, and challenge stereotypes. Examination of the identification and mitigation of bias in data science, emphasizing real-world data related to justice, policing, hunger, and poverty.
Prerequisites: DSCI 140.
Usually offered: Annually, spring semester.
Semester credits: 4.
DSCI 244 Practicum/Internship
Content: Supervised practical experience in consultation with a faculty member, or faculty-supervised internship off campus.
Prerequisites: None.
Usually offered: Annually, fall and spring semester.
Semester credits: 1-4.
DSCI 245 Applied Data Science Practicum
Content: Academic and experiential bridge between classroom theory and real-world application in 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 students with opportunities to frame research questions, manage and clean data, execute analyses, and communicate results.
Prerequisites: DSCI 140.
Usually offered: Annually.
Semester credits: 2.
DSCI 250 Intermediate Data Science
Content: Extends foundational skills to more advanced tools and practices. Topics include advanced data wrangling, working with larger and more complex datasets, writing reusable functions, and accessing data from databases. Students learn to manage reproducible workflows, use version control for collaborative work, and create well-documented projects. Emphasizes interactive and dynamic visualization and the iterative process of posing questions, manipulating data, and refining analysis.
Prerequisites: DSCI 140.
Semester credits: 4.
DSCI 299 Independent Study in Data Science
Content: Independent study in data science under the supervision of a faculty advisor. Students pursue an individualized project involving data acquisition, management, analysis, and interpretation. Projects may emphasize methodological development, applied analysis in a substantive domain, or replication and extension of existing research, and must employ appropriate computational tools and reproducible workflows.
Prerequisites: None.
Usually offered: Annually, fall and spring semester.
Semester credits: 1-4.
DSCI 304 Applied Statistical Modeling and Inference
Content: Unification of regression-based inference and predictive modeling across disciplines. Students will learn to build, interpret, and critique multivariate models balancing explanation and prediction. Topics include multiple and logistic regression, model selection, diagnostics (multicollinearity, heteroscedasticity, influential observations), and cross-validation. Extensions such as regularization (ridge, lasso), interaction and polynomial terms, and principal components analysis (PCA). Emphasis placed on applied modeling decisions, clear communication of results, and reproducible analysis in R.
Prerequisites: MATH 105, ECON 103, HEAL 200, MATH 255, POLS 201, or PSY 200.
Usually offered: Annually, spring semester.
Semester credits: 4.
DSCI 400 Data Science Thesis
Content: Independent, faculty-supervised research experience in which students design and execute an original, data-driven investigation. Students formulate a well-defined research question, collect, construct, or compile original data sources, and apply appropriate analytical and computational methods using a fully documented and reproducible workflow. Projects may emphasize prediction, inference, causal analysis, or methodological innovation and must be grounded in relevant academic and applied literature. The thesis culminates in a substantial written document and a public, reproducible project repository, as well as an oral defense presented to faculty and peers. The course is offered over two consecutive semesters (fall and spring) and that a deferred grade will be awarded after fall semester.
Prerequisites: DSCI 250.
Restrictions: Senior standing required.
Usually offered: Annually, fall and spring semester.
Semester credits: 2.
DSCI 440 Data Science Capstone
Content: Culminating experience of the major. Students design and execute an original research project, collecting or constructing data to address a substantive question. Projects employ advanced analytical methods - such as regression, machine learning, natural language processing, spatial or network analysis, simulation, or causal inference - within a fully reproducible and documented workflow. Grounded in scholarly literature, each project results in a substantial written paper, a public repository, and a professional presentation, emphasizing methodological rigor, transparency, and responsible data practice.
Prerequisites: DSCI 250.
Usually offered: Annually, spring semester.
Semester credits: 4.