Introducing the FUS Faculty: Charles Burke

Assistant Professor, Data Science

PhD Geography and Earth Science, McMaster University
MA Political Science, University of Windsor
BA Environmental Studies, Bishops University

Office: North Campus Villa Loft, Office 14
Phone: +41 78 249 2293
Email: cburke@fus.edu

Charles is an Assistant Professor of Data Science. He received his PhD in Transportation Geography from McMaster University in Hamilton, Ontario, Canada. For his dissertation, Charles used Open Data to simulate and analyse the City of Toronto’s traffic network, with the goal of creating safer and more efficient road cycling for urban cyclists. He turned that research into a brief tenure as Founder and CEO of the bike share company - Start the Cycle in 2014.

His work has been published in journals such as Transportation, The Professional Geographer, and Transport Policy, as well as publications that focus on business and innovation, and Canadian Geographic magazine. His research and teaching interests include Quantitative Methods and Data Visualisation, as well as Quantitative Geography and Spatial Analysis, with a particular focus on sustainable transportation, demographic change, and human-computer interactions.

Charles has received awards for both his teaching and research, as well as athletics. His past research received a Social Sciences and Humanities Research Council Fellowship from the Government of Canada, and in 2021 Charles won an Annual Teaching Excellence Award for the University Scholars Programme, and in 2022 an Annual Digital Educator’s Award at the National University of Singapore for teaching excellence. While receiving his Bachelor’s Degree, Charles was a two time Academic All Canadian and Lord Strathcona Award winner as Quebec’s top scholar athlete for Football.

Charles joins FUS from the National University of Singapore after working five years as a Quantitative Reasoning Lecturer for the University Scholars Programme, and after serving as its Director of the Quantitative Reasoning Centre in 2020.

2023-2024 Courses

MAT 201	Introduction to Statistics	FALL 2023
This computer-based course presents the main concepts in Statistics: the concept of random variables, frequency, and probability distributions, variance and standard deviation, kurtosis and skewness, probability rules, Bayes theorem, and posterior probabilities. Important statistical methods like Contingency analysis, ANOVA, Correlation analysis and Regression Analysis are introduced and their algorithms are fully explained. The most important probability distributions are introduced: Binomial, Poisson, and Normal distribution, as well as the Chebyshev theorem for non-known distributions. Inferential statistics, sampling distributions, and confidence intervals are covered to introduce statistical model building and single linear regression. Active learning and algorithmic learning are stressed. Emphasis is put both on algorithms –methods and assumptions for their applications. Excel is used while calculators with STAT buttons are not allowed. Ultimately students are required to make a month-long research project, select the theoretical concept they want to test, perform a literature review, find real data from Internet databases or make their surveys, apply methods they studied in the class, and compare theoretical results with their findings. Research is done and presented in groups, papers are Individual. Selected SPSS or Excel Data Analysis examples are also provided.
MGT 515	Business Analytics	FALL 2023
Business Analytics is a necessary skill for everyone in our big data world where evidence-based decision-making is crucial regardless of whether you are working in private business, in an NGO/Cultural Institution, or in any level of Government. In this course, students will learn how to acquire data, clean and validate the collected data, analyze them, and communicate the results obtained from the data. These four steps will be performed using Business Intelligence softwares.
BUS 306	Quantitative Methods and Dynamic Forecasting	SPRING 2024
In the first part of this course students learn concepts in inferential statistics, its main principles and algorithms. They learn how to apply sampling distributions in the case of business random variables, how to state and test business hypotheses about population mean or proportion differences, how to calculate ANOVA table components, and how to deploy estimation methods to provide information needed to solve real business problems. In the second part of the course, students learn advanced model building methods, algorithms needed to make and test dynamic multiple regression models and time series (ARMA) models. In addition to teaching and learning methods based on the textbook, problem-based learning (PBL) and interactive engagement (IE) are used. Many internet data bases, EXCEL add-ins and EViews are used to enhance IE based learning. Selected SPSS or STATA examples are also provided.
BUS 397	Data Mining (Business Intelligence)	SPRING 2024
This course introduces the cutting-edge computing methods for the analysis of business and marketing big data which help in inferring and validating patterns, structures and relationships in data, as a tool to support decisions at all levels of management. Students learn key descriptive, predictive, and prescriptive data mining methods with both supervised and non-supervised machine learning algorithms, which produce information for non-structured and semi structured decision making. While the course introduces a systems approach to business data processing, emphasis will be given to empirical applications using modern software tools such as Data Mining in Solver-Analytics More specifically, students will become familiar with and demonstrate proficiency in applications such as Cluster Analysis, Market Basket Analysis. Logistic Regression, Naïve Bayes Classification, Entropy Calculation, Classification Trees. Engagement-based learning is provided by using real world cases as well as computer based hands-on for real data analysis. Ultimately, working in teams, students will make the month long projects in applying Data Mining analytical techniques on the real world business problems, and will make suggestions for improvement which will be backed by the new information, gained from DM. Projects are presented in groups. Research papers, which are based on the projects, are individual.

Publications:

Internet and Happiness in West Africa: The Next Billion Users are Not a Single Entity, WC Ng, CMD Burke, IEEE GHTC, 2020

Identifying “sensible locations” for separated bike lanes on a congested urban road network: A Toronto case study, CM Burke, DM Scott, The Professional Geographer 70 (4), 541-551, 2018

Regional economic impacts of a transportation infrastructure project: the Herb Gray Parkway, WP Anderson, H Maoh, C Burke, Transportation, Knowledge and Space in Urban and Regional Economics, 2018

Identifying Sensible Locations for Separated Bike Lanes on a Congested Urban Road Network: Case Study in Toronto, Canada, CM Burke, DM Scott, Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017

Immigrant status and commute distance: an exploratory study based on the greater Golden Horseshoe, K Bruce Newbold, DM Scott, C Burke, Transportation 44 (1), 181-198, 2017

ADDRESSING DRIVER CONCERNS: THE NETWORK ROBUSTNESS INDEX APPROACH TO PLANNING CITY CYCLING INFRASTUCTURE, C Burke, 2017

The space race: A framework to evaluate the potential travel-time impacts of reallocating road space to bicycle facilities, CM Burke, DM Scott, Journal of Transport Geography 56, 110-119, 2016

Is the Bloor Danforth Corridor a'Sensible Location'for a Separated Bike Lane?, CM Burke, DM Scott, 2016

A comparative analysis of cross border travel influences at the port level: Pacific Highway/Douglas, BC—Blaine, WA and Windsor, ON—Detroit, MI, CM Burke, Research in transportation business & management 16, 95-101, 2015

Passenger car flows across the Canada–US border: The effect of 9/11, WP Anderson, HF Maoh, CM Burke, Transport Policy 35, 50-56, 2014

Passenger Car Flows Across Canada-United States Border: Effect of 9/11 Terrorist Attacks, WP Anderson, H Maoh, CM Burke, Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013

Modeling the resilience of surface freight transportation systems: an application to Ontario, Canada, H Maoh, W Anderson, C Burke, 47th Annual Canadian Transportation Research Forum Conference, Calgary …, 2012

Assessing risk and resilience for transportation infrastructure in Canada, WP Anderson, H Maoh, C Burke, Transportation and Innovation: The Roles of Governments, Industry and … , 2011

Does Security Trump Travel?: A Critical Review of Canada-US Border Impacts on Trade and Travel, C Burke, University of Windsor, 2010

A complete list of his publications can be found here.

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Charles Burke

Assistant Professor, Data Science

2023-2024 Courses

Publications: