2023-2024 Edition

Academic Catalog

Data Analysis Minor

Department website: http://www.wesleyan.edu/qac/index.html


The Data Analysis Minor provides a basic introduction to data analysis, including the theory and practical skills needed to collect and prepare data for analysis, explore and visualize data, build models and test hypotheses, discover insights, and communicate results in meaningful ways. The Data Analysis Minor is designed to strengthen the student's ability to apply statistical, mathematical, and programming methods to traditional areas of empirical research within their fields.

Minor Requirements

Basic Knowledge Courses
Select one of the following:1
Quantitative Methods for the Biological and Environmental Sciences
Elementary Statistics
Statistics: An Activity-Based Approach
Applied Data Analysis
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
Applied Vectors and Matrices
Statistical Foundations
Introductory Econometrics
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 Econometrics
Introduction to Forecasting in Economics and Finance
Empirical Methods for Political Science
Advanced Topics in Media Analysis
Computational Physics
Introduction to Survey Design and Analysis
Introduction to (Geo)Spatial Data Analysis and Visualization
Proseminar: Machine Learning Methods for Audio and Video Analysis
Introduction to Network Analysis
Data Visualization: An Introduction
QAC251ZData Visualization: An Introduction1
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)
Applied Time Series Analysis
Bayesian Data Analysis: A Primer (0.5 credit)
Advanced R: Building Open-Source Tools for Data Science
can count QAC 380 or 381, not both
Introduction to Statistical Consulting
QAC Praxis Service Learning Lab
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

Additional Information

  • 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.
  • Only graded courses can satisfy the requirements for the data analysis minor and the applied data science certificate. Courses completed with a CR/U grading mode will not satisfy the requirements of the two programs.