Nexus in 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:

Valerie Barr, Jean E. Sammet Professor of Computer Science, Teaching Spring Only

Martha Hoopes, Professor of Biological Sciences

Barbara Lerner, Professor of Computer Science

Jessica Sidman, Professor of Mathematics on the John Stewart Kennedy Foundation, Teaching Fall Only

Eleanor Townsley, Andrew W. Mellon Professor of Sociology and Director of Nexus, Teaching Fall Only

Mara Breen, Associate Professor of Psychology and Education

KC Haydon, Associate Professor of Psychology and Education

Katherine Lande, Associate Professor of Economics, Teaching Spring Only

Heather Pon-Barry, Associate Professor of Computer Science, Teaching Fall Only

Andy Reiter, Associate Professor of Politics and International Relations

Steven Schmeiser, Associate Professor of Economics, On Leave 2021-2022

Dylan Shepardson, Robert L. Rooke Associate Professor of Mathematics

Kate Singer, Associate Professor of English, Teaching Fall Only

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 Education2
A presentation at LEAP Symposium
Total Credits18

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

Courses other than those listed below may count toward the Nexus. Students should consult the Nexus track chair for consideration of courses not on the list.

ASTR-228Astrophysics I: Stars and Galaxies4
Biological Sciences
Computer Science
COMSC-100Computing and the Digital World4
COMSC-106Fundamentals of Applied Computing4
COMSC-151DSIntroduction to Computational Problem Solving: 'Big Data'4
COMSC-201Advanced Problem-Solving and Elementary Data Structures4
COMSC-205Data Structures4
COMSC-211Advanced Data Structures4
COMSC-243EMTopic: 'Embodied Interaction'4
COMSC-311Theory of Computation4
COMSC-334Artificial Intelligence4
COMSC-335Machine Learning4
COMSC-341NLTopics: 'Natural Language Processing'4
COMSC-343Programming Language Design and Implementation4
ECON-220Introduction to Econometrics4
Environmental Studies
ENVST-200Environmental Science4
GEOG-205Mapping and Spatial Analysis4
GEOG-210GIS for the Social Sciences and Humanities4
GEOG-320Research with Geospatial Technologies4
GEOL-131Introduction to Hydrology: A Data Perspective4
International Relations
IR-200Research Methods4
MATH-211Linear Algebra4
MATH-301Real Analysis4
MATH-339PTTopics in Applied Mathematics: 'Optimization'4
PHIL-180DETopics in Applied Philosophy: 'Data Ethics'4
PSYCH-204Research Methods in Psychology4
PSYCH-310APLaboratory in Social Psychology: 'Community-Based Participatory Action Research'4
PSYCH-310QRLaboratory in Social Psychology: 'Qualitative Research in Psychology'4
PSYCH-326BHLaboratory in Personality and Abnormal Psychology: 'Behavioral Methods for Social and Intergroup Psychology'4
PSYCH-330RDLab in Developmental Psychology: 'Laboratory in Romantic Development: Observational Coding Methodology'4
SOCI-225Social Science Research and Data Analysis4
STAT-240Elementary Data Analysis and Experimental Design4
STAT-241Methods in Data Science4
STAT-242Intermediate Statistics4
STAT-340Applied Regression Methods4
STAT-343Mathematical Statistics4
STAT-344SMSeminar in Statistics and Scientific Research: 'Survey Sampling'4