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David Marsh

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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 forecast 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.