2021-2022 Edition

Academic Catalog

Certificate in Applied Data Science

Introduction

Like the Data Analysis Minor, the Applied Data Science Certificate 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 Certificate in Applied Data Science adds to these skills by extending the necessary computational background and exposure to "Big Data" topics (deep learning, high-performance computing etc.). It incorporates additional courses in machine learning, and the development of strong statistical computing and programming skills.   Through the Practicum experience, students have an opportunity to work closely with a faculty member, with real world data applying these skills to their own areas of interest.

Certificate Requirements

To earn the Applied Data Science Certificate, students must complete seven graded courses and the capstone Data Analysis Practicum. Before admitted to the program students are asked to submit a tentative plan of study.

Select one of the following basic knowledge courses: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
Select two courses from the following mathematical, statistical and computing foundation courses, each from a different group:2
Mathematical Foundations
Vectors and Matrices
Linear Algebra
Discrete Mathematics
Graph Theory
Statistical Foundations
Quantitative Methods in Economics
An Introduction to Probability
Mathematical Statistics
Computing Foundations
Bioinformatics Programming
Introduction to Programming
How to Design Programs
Computer Science I
Computer Science II
Select two of the following applied data science courses:2
Exploratory Data Analysis and Pattern Discovery
Applications of Machine Learning in Data Analysis
Quantitative Textual Analysis: Introduction to Text Mining
Select two credits from the following applied electives:2
Introduction to GIS
Advanced GIS and Spatial Analyses
Economics of Big Data
Econometrics
Introduction to Forecasting in Economics and Finance
Empirical Methods for Political Science
Advanced Topics in Media Analysis
Computational Physics (0.5 credits)
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
Experimental Design and Causal Inference
Longitudinal Data Analysis (0.5 credits)
Hierarchical Linear Models (0.5 credits)
Latent Variable Analysis (0.5 credits)
Survival Analysis (0.5 credits)
Applied Time Series Analysis
Bayesian Data Analysis: A Primer (0.5 credits)
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
NOTE: at least one of the electives should be a 300 level course
The capstone Data Analysis Practicum that includes an ethics and epistemology seminar discussion as well as completing an independent data science project.1

Additional Information

  • Some of the courses that count toward the certificate may have a prerequisite, such as calculus. These prerequisites do not count toward the certificate, and students attempting to complete the certificate are not recused from these prerequisites.
  • Mathematics majors cannot count courses in the foundations groups already covered by their major toward the certificate. 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 certificate. 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.
  • It is strongly recommended that students who are not mathematics or computer science majors take courses in the computing foundations group to satisfy the certificate requirements. They can also substitute either MATH232 or COMP212 for one of their applied elective courses.
  • Economics majors and minors cannot count ECON300 toward the certificate and must instead complete one course from each of the other two foundation groups.
  • Students cannot count more than one course towards this certificate that also counts toward completion of any of their majors or minors.
  • One course taken elsewhere may substitute as appropriate for any of the above courses and count toward the certificate, subject to the QAC Advisory Committee’s approval (where routine approval may be delegated to the QAC director).
  • Students can substitute a course from among the applied data science and applied elective courses for the basic knowledge course, subject to approval.
  • 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.
  • Students cannot receive both the data analysis minor and the applied data science certificate.

contact

Director of the QAC