Nexus in Data Science
Eleanor Townsley, Nexus director
Amber Douglas, track chair
Martha Hoopes, track chair
217G Dwight Hall
413-538-3010
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
Faculty
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:
| Code | Title | 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 | ||
| Completion of the UAF application stages 1 and 2 1 | ||
| A substantive internship | ||
| COLL-211 | Reflecting Back: Connecting Internship and Research to Your Liberal Arts Education | 2 |
| A presentation at LEAP Symposium | ||
| Total Credits | 18 | |
- 1
Or a fifth class with approval of the track chair
- 2
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
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.
| Code | Title | Credits |
|---|---|---|
| Astronomy | ||
| ASTR-226 | Cosmology | 4 |
| ASTR-228 | Astrophysics I: Stars and Galaxies | 4 |
| Biological Sciences | ||
| BIOL-223 | Ecology | 4 |
| BIOL-234 | Biostatistics | 4 |
| Computer Science | ||
| COMSC-100 | Computing and the Digital World | 4 |
| COMSC-106 | Fundamentals of Applied Computing | 4 |
| COMSC-133DV | Data Visualization: Design and Perception | 4 |
| COMSC-205 | Data Structures | 4 |
| COMSC-235 | Applications of Machine Learning | 4 |
| COMSC-311 | Theory of Computation | 4 |
| COMSC-312 | Algorithms | 4 |
| COMSC-334 | Artificial Intelligence | 4 |
| COMSC-335 | Machine Learning | 4 |
| COMSC-341NL | Topics: 'Natural Language Processing' | 4 |
| COMSC-343 | Programming Language Design and Implementation | 4 |
| Economics | ||
| ECON-220 | Introduction to Econometrics | 4 |
| ECON-320 | Econometrics | 4 |
| Environmental Studies | ||
| ENVST-200 | Environmental Science | 4 |
| Geography | ||
| GEOG-205 | Mapping and Spatial Analysis | 4 |
| GEOG-210 | GIS for the Social Sciences and Humanities | 4 |
| Mathematics | ||
| MATH-211 | Linear Algebra | 4 |
| MATH-301 | Real Analysis | 4 |
| MATH-339PT | Topics in Applied Mathematics: 'Optimization' | 4 |
| MATH-342 | Probability | 4 |
| Politics | ||
| POLIT-200 | Research Methods | 4 |
| POLIT-387EC | Advanced Topics in Politics: 'U.S. Elections' | 4 |
| Psychology | ||
| PSYCH-201 | Statistics | 4 |
| PSYCH-204 | Research Methods in Psychology | 4 |
| PSYCH-219GR | Topics in Social Psychology: 'Behavior Research in Intergroup Psychology' | 4 |
| PSYCH-310QR | Laboratory in Social Psychology: 'Qualitative Research in Psychology' | 4 |
| PSYCH-330RD | Lab in Developmental Psychology: 'Laboratory in Romantic Development: Observational Coding Methodology' | 4 |
| Sociology | ||
| SOCI-225 | Social Science Research and Data Analysis | 4 |
| SOCI-316AG | Special Topics in Sociology: 'Society of Algorithms' | 4 |
| SOCI-316ST | Special Topics in Sociology: 'Storytelling Sociology: Data for the People' | 4 |
| Statistics | ||
| STAT-242 | Intermediate Statistics | 4 |
| STAT-331 | Design of Experiments | 4 |
| STAT-340 | Applied Regression Methods | 4 |
| STAT-343 | Mathematical Statistics | 4 |
| STAT-344TM | Seminar in Statistics and Scientific Research: 'Time Series Analysis' | 4 |
