Advisory Board:
Francis Starr, Professor of Physics, Chair
​Erika Franklin Fowler, Associate Professor of Government
Daniel Krizanc, Professor of Computer Science
Manolis Kaparakis, Director of Centers for Advanced Computing, ex officio
David Baird, Vice President for Information Technology and CIO
Marc Eisner, Professor of Government and Dean of the Social Science
Diane Klare, Head of Research Services, Olin Memorial Library

Department/Program Home Page

Department/Program Description

The Quantitative Analysis Center (QAC) coordinates support for quantitative analysis across the curriculum and provides an institutional framework for collaboration across departments and disciplines in the area of data analysis. Through its programs, it facilitates the integration of quantitative teaching and research activities and provides experiential learning opportunities in statistical computing across academic fields and disciplines. The Center contributes to the development of digital and computational studies initiatives, sponsors data analysis labs, and oversees the Data Analysis Minor and the Applied Data Science Certificate programs.

Minor Requirements
Basic Knowledge Courses
Select one of the following: 1
Elementary Statistics
Modeling and Data Analysis: From Molecules to Markets
Statistics: An Activity-Based Approach
Applied Data Analysis
Digging the Digital Era: A Data Science Primer
An Introduction to Data Journalism
Mathematical, Statistical, and Computing Foundation Courses
Select two courses from the following, each from a different group: 2
Mathematical Foundations
Vectors and Matrices
Linear Algebra
Discrete Mathematics
Graph Theory
Statistical Foundations
Quantitative Methods in Economics
Political Science by the Numbers
An Introduction to Probability
Mathematical Statistics
Computing Foundations
Bioinformatics Programming
Introduction to Programming
How to Design Programs
Computer Science I
Computer Science II
Applied Electives
Select two credits from the following: 2
Introduction to GIS
Advanced GIS and Spatial Analyses
Economics of Big Data
Introduction to Forecasting in Economics and Finance
Empirical Methods for Political Science
Advanced Topics in Media Analysis
Computational Physics
Introduction to (Geo)Spatial Data Analysis and Visualization
Proseminar: Machine Learning Methods for Text, Audio and Video Analysis
Introduction to Network Analysis
Data Visualization: An Introduction
Exploratory Data Analysis and Pattern Discovery
Experimental Design and Causal Inference
Longitudinal Data Analysis (0.5 credit)
Hierarchical Linear Models (0.5 credit)
Latent Variable Analysis (0.5 credit)
Survival Analysis (0.5 credit)
Bayesian Data Analysis: A Primer (0.5 credit)
Advanced R: Building Open-Source Tools for Data Science
Introduction to Statistical Consulting
Applications of Machine Learning in Data Analysis
Quantitative Textual Analysis: Introduction to Text Mining
NOTE: at least one of the electives should be a 300 level course


  • There may be prerequisite courses required for some of the courses that count toward the minor, such as calculus. These prerequisites do not count toward the minor, and students attempting to complete the minor are not recused from these prerequisites.
  • Mathematics majors cannot count courses in the foundations groups already covered by their major toward the minor. They must instead complete one course from the statistical foundations group and complete three applied elective courses. Alternatively to completing three applied elective courses, they can take either MATH232 or COMP212 and complete two applied elective courses.
  • Computer science majors cannot count courses in the foundations groups already covered by their major toward the minor. They must instead complete one course from the statistical foundations group and complete three applied elective courses. Alternatively, they can complete both MATH231 and MATH232 and complete two applied elective courses.
  • Economics majors and minors cannot count ECON300 toward the minor and must instead complete one course from each of the other two foundation groups.
  • Students cannot count more than one course toward this minor that is also counted toward completion of any other of their majors or minors.
  • One course taken elsewhere may substitute as appropriate for any of the above courses and count toward the minor, subject to the QAC Advisory Committee’s approval (where routine approval may be delegated to the QAC Director).
  • A more advanced course can substitute for the basic knowledge course, subject to approval. Students with good quantitative skills are strongly encouraged to do this.
  • Students cannot receive both the data analysis minor and the Applied Data Science Certificate.