Data Science

Eleanor Townsley, Nexus director

Amber Douglas, track chair

Martha Hoopes, track chair

217G Dwight Hall

Overview and Contact Information

Data Science is an emerging discipline that integrates computational, programming, and statistical skills in applications across a range of fields. This discipline uses different types of data to create an accessible narrative and helps pose new questions, identify patterns, visualize trends, and make predictions using new techniques. Data scientists have the potential to offer novel insights, expand our ability to ask questions that push the limits of our understanding, and harness the creativity, critical thinking, and communication skills that form the core of a liberal arts education. The vast quantities of data created by modern life make Data Science possible but also drive the need for an approach to the discipline that takes privacy and other ethical considerations seriously.

See Also


This area of study is administered by the Data Science committee:

Andrea Foulkes, Professor of Mathematics and Statistics

Janice Gifford, Professor of Statistics

Martha Hoopes, Professor of Biological Sciences, Teaching Fall Only

Jessica Sidman, Professor of Mathematics

Eleanor Townsley, Professor of Sociology, Teaching Spring Only

Amber Douglas, Associate Professor of Psychology and Education; Director of Student Success Initiatives

Barbara Lerner, Associate Professor of Computer Science

Katherine Schmeiser, Associate Professor of Economics

Kate Singer, Associate Professor of English, Teaching Fall Only

Mara Breen, Assistant Professor of Psychology and Education

KC Haydon, Assistant Professor of Psychology and Education

Heather Pon-Barry, Assistant Professor of Computer Science

Andy Reiter, Assistant Professor of Politics and International Relations

Steven Schmeiser, Assistant Professor of Economics and Complex Organizations

Dylan Shepardson, Assistant Professor of Mathematics

Daniel Sheldon, Five College Assistant Professor of Computer Science

Eitan Mendelowitz, Visiting Assistant Professor of Data Science

Samuel Tuttle, Visiting Assistant Professor of Data Science

Requirements for the Nexus

A minimum of 18 credits:

Four 4-credit courses, of which:16
one must be in statistics at the 200 level or higher, from the list of courses approved for this Nexus
one must be in computer science at the 200 level or higher, from the list of courses approved for this Nexus
one must be in an application area (e.g., biology, economics, English, psychology, sociology) at the 200 level or higher, from the list of courses approved for this Nexus
one is an elective course that demonstrates an interest in data science and that may be taken at the 100 level and must be taken before the internship
Note: at least one of these four courses must be an approved 300-level capstone course that goes into depth in statistics, computer science, or a data science application area. Appropriate courses include: COMSC-335, ECON-320, SOCI-316NT, STAT-340 or STAT-344 2
Completion of the UAF application stages 1 and 2 1
A substantive internship
COLL-211Reflecting Back: Connecting Internship and Research to your Liberal Arts Education 2
A presentation at LEAP Symposium
Total Credits18

 Or a fifth class with approval of the track chair


Other capstone courses would require prior approval from the Nexus committee

Additional Specifications

  • In one of the four courses for this Nexus, students must work intimately with data to explore, visualize, contextualize, and present conclusions.
  • The sequence of a Nexus is part of what makes it unique. Students must complete at least one of their four courses towards the Nexus and UAF application stages 1 and 2 before the internship or research project. COLL-211 is taken after the internship or research project and culminates in a presentation at LEAP Symposium.

Courses Counting toward the Nexus

ASTR-226Cosmology 4
ASTR-228Astrophysics I: Stars and Galaxies 4
BIOL-223Ecology 4
BIOL-234Biostatistics 4
COMSC-201Advanced Problem-Solving and Object-Oriented Programming 4
COMSC-211Data Structures 4
COMSC-311Theory of Computation 4
COMSC-312Algorithms 4
COMSC-334Artificial Intelligence 4
COMSC-335Machine Learning 4
COMSC-336Intelligent Information Retrieval 4
COMSC-341NLTopics: 'Natural Language Processing' 4
COMSC-341SPTopics: 'Computer Security & Privacy' 4
COMSC-343Programming Language Design and Implementation 4
ECON-220Introduction to Econometrics 4
ECON-320Econometrics 4
ENVST-200Environmental Science 4
GEOG-205Mapping and Spatial Analysis 4
GEOG-210GIS for the Social Sciences and Humanities 4
GEOG-320Research with Geospatial Technologies 4
HIST-257Research Methods in History, Environmental Change, and Public Health 4
IR-200Research Methods 4
MATH-211Linear Algebra 4
MATH-301Real Analysis 4
MATH-339PTTopics in Applied Mathematics: 'Optimization' 4
MATH-342Probability 4
PSYCH-200Research Methods in Psychology 4
PSYCH-201Statistics 4
PSYCH-310APLaboratory in Social Psychology: 'Applied Social Psychology' 4
PSYCH-310SPLaboratory in Social Psychology 4
PSYCH-326PRLaboratory in Personality and Abnormal Psychology: 'Personality Research' 4
PSYCH-330RDLab in Developmental Psychology: 'Laboratory in Romantic Development: Observational Coding Methodology' 4
SOCI-225Survey Research and Data Analysis 4
SOCI-316NTSpecial Topics in Sociology: 'Social Network Analysis: Analyzing Who You Know and How It Matters' 4
STAT-240Elementary Data Analysis and Experimental Design 4
STAT-241Methods in Data Science 4
STAT-242Intermediate Statistics 4
STAT-340Applied Regression Methods 4
STAT-343Mathematical Statistics 4
STAT-344SMSeminar in Statistics and Scientific Research: 'Survey Sampling Statistics' 4