| Name | David Marsh |
| School | School of Applied Computer Science & IT |
| Program | - Bachelor of Data Analytics
- Big Data Solutions Architecture
- Digital Solutions Management
|
| Academic and professional designations | - PhD Candidate: Mathematical and Statistical Modelling
- MSc: Statistics and Data Analysis
- Bachelor of Mathematics
- Member: Statistical Society of Canada
|
| Title | Professor |
| Courses taught | - ETHS8010 - Ethics and Security
- ETHS8130 - Ethics and Security in Reporting
- INFO8120 - Emerging Trends in Big Data
- INFO8606 - Digital Solutions Management Capstone
- MATH3160 - Numerical Methods
- PROG8435 - Data Analysis Mathematics, Algorithms and Modeling
- PROG8630 - Data Visualization and Reporting
- SENG8080 - Case Studies in Big Data
- STAT3000 - Applied Statistics
|
| Areas of expertise & interest |
Predictive analytics, mathematical modeling, measures of fairness, generalized linear models, recommender systems, ethics of technological adoption
|
| Industry experience, professional currency activities |
20+ years building and leading predictive analytic and data science teams in various industries
|
| Major research projects, scholarly activities, and/or publications | - Introduction to Recommender Systems, Presentation
- SSC 2022 “Case Study #2: Towards a clear understanding of rural internet – What statistical measure can be used to assess, compare and forecaset internet speed for rural Canadian communities”, Coach and Mentor
- Web Traffic Multi-Layered Time Series Predictions, Research
- Naïve-Bayes Classification, Presentation
- Customer Relationship Management for Multi-Client NFP, Research
- Growth in Opioid-Related Mortality, Research
- Multi-Level Risk Models with Internal and External Data, Research
- Internal Behaviour-Based Customer Segmentation, Research
- Product Level Risk Modeling, Research
- Using the LOGISTIC Procedure to Model Responses to Financial Services Direct Marketing, Conference Paper
|
| Additional information | David Marsh enjoys combining strong theoretical understanding with practical problems to apply domain-specific techniques to novel areas. |
