Program Information
The Graduate Diploma (GDip) in Computational Data Analytics for the Social Sciences and Humanities (CDASH) will train students in coding, data interpretation and visualization from a multi-disciplinary perspective that will enable them to work in teams and to communicate key findings to stakeholders in a manner that leads to actionable impacts. The GDip in CDASH will also give students different theoretical frameworks from the social sciences/humanities that will allow them to analyze societal issues relating to data collection and use by firms and governments.
Questions? Email the program director, Prof. Tom Parker.
Admission requirements
- The Graduate Diploma (GDip) in Computational Data Analytics for the Social Sciences and Humanities (CDASH) is offered in conjunction with any University of Waterloo master's or doctoral program.
- Students must register by completing an online registration form. The application should identify the courses that the student would like to take in fulfillment of the GDip requirements.
- Students must be in good standing in their home master's or doctoral program to take courses for the GDip in Computational Data Analytics for the Social Sciences and Humanities.
Diploma requirements
- Students must complete 3 graduate level courses (0.50 unit weight) in addition to the degree requirements of their home master's or doctoral program. There can be no double counting for the diploma and the degree.
- Students must complete 3 of the following 17 courses (or other courses that fit with the goals of this GDip, and as approved by the Program Director):
- ACC 640 - Unstructured Data and Natural Language Processing
- ECON 626 - Machine Learning for Economists
- ECON 673 - Econometric Methods for High Dimensional Data
- GEOG 606 - Scientific Data Wrangling
- GEOG 607 - Fundamentals of Geographic Information Systems
- HIST 640 - Digital History
- HLTH 605B - Quantitative Methods and Analysis
- HLTH 650 - Applied Machine Learning and Artificial Intelligence in Public Health
- INTEG 640 - Computational Social Science
- INTEG 641 - Hard Decisions and Wicked Problems
- PS 627 - Introduction to Coding and Programming
- PS 629 - Data Mining and Statistical Methods (not to be taken by ECON grad students)
- PSCI 637- Introduction to Machine Learning for Public Policy
- PSYCH 640 - Data Analysis and Graphing in R
- SOC 710 - Applied Statistical Methods
- STAT 830 - Experimental Design
- STAT 831 - Generalized Linear Models and Applications
- STAT 842 - Data Visualization
- STAT 847 - Exploratory Data Analysis
- Students must maintain an average of 70% across courses for this GDip.