Dr. Dzieciolowski’s career combines 30 years of experience in data science and leadership roles in the Canadian telecommunication industry with teaching and research, as a part-time faculty member at John Molson School of Business in Montreal.
In his professional career, he led Data Science and Modelling groups in Rogers, Fido and Bell Canada where he developed predictive models and analytical solutions for key business areas of business such as customer retention, revenue growth and acquisition of new customers.
Currently, Dr Dzieciolowski serves as a Chief Analytics Officer at Daesys Inc., the startup company developing Machine Learning Operations (MLOps) platform for algorithms used AI-advanced companies.
At JMSB, Dr. Dzieciolowski researches and teaches courses in data mining, statistics, data science and analytical software to undergraduate and graduate students. He has built synergies between industry and academia to provide internship funding for students of Montreal universities. He has developed and launched a digital, joint Certification in Business Analytics between JMSB and SAS – the world’s leader in data science software. There were over 100 students who graduated with the Certification in 2021.
Dr. Dzieciolowski’s research interests focus on statistics and data science methods; in particular, on models diagnostics and explainability, big data, predictive models, multivariate analysis, forecasting, econometric models, social networks and text analytics. He was distinguished with CUPFA Special JMSB Research award for the project in big data in 2016.
He obtained MSc in mathematics from Warsaw University in Poland and PhD in statistics from Queen’s University in Kingston, Ontario.
He is an active promoter of data science and a frequent speaker at academic and industry conferences.
Statistical Software for Data Management and Analysis
The objective of the course is to gain a basic understanding of the concepts, principles and techniques of data management and statistical analyses through the use of Statistical Software. The focus of this course is on practical aspects of data management and statistical analysis with the help of Statistical Analysis System (SAS) – widely used by business, government and academia. In the process of familiarizing with SAS, principal attention will be given to data management techniques related to handing of data storage, information retrieval, variable and row transformation,data cleansing, file handling and preparation of data for analytics, visualization,statistical analysis and reporting. The course helps students understand methods of handling big data through the use of principles of efficient programming.Through hands-on approaches students learn how to design and execute programs for the data-intensive processes.
Data Mining Techniques
The objective of the course is to introduce basic concepts and techniques of Data Mining and its applications in business. In the recent years Data Mining, often also referred to as Data Science has become one of the most dynamically growing areas of business knowledge discovery field. Proliferation of computing resources, Internet, social networks, mobile communication with the resulting deluge of data, often called -Big Data, has motivated companies to turn data into an asset for decision making and used gain competitive advantage.
The course focuses on supervised and unsupervised data mining methods, their underlying principles and applications for structured data as well for unstructured data such as text.The data mining techniques introduced throughout the course include regression models and learning algorithms such as k-nearest neighbors, naïve Bayes,classification and regression trees, support vector machine, neural networks,discriminant analysis, association rules, cluster analysis and text mining.
BRIDGING ACADEMIC AND INDUSTRY EXPERTISE FOR BIG DATA PROBABILISTIC DATA MATCHING - CUPFA grant for special project in Big Data
K. Dzieciolowski, Y. P. Chaubey, F. Nebebe and D. Sen, 2009, A Semi-Bayesian Approach for Estimation of Joint Distribution from Marginal Distributions, International Journal of Statistical Sciences ISSN 1683–5603, Vol. 9;
K. Dzieciolowski, J.K. Galbraith, (2004), Indicators of Competition in Wireline/Wireless Market for Telecommunication Services, CIRANO
K. Dzieciolowski, W.H. Ross, (1990), Assessing Case Influence on Confidence Intervals in Nonlinear Regression, Canadian Journal of Statistics, v18, 2, 127-139
Big Data Analytics, Data Mining and Computational Intelligence-IADIS, Lisbon 2022
K. Dzieciolowski, Classifier Rank - A New Classification Assessment Method"
Big Data Analytics, Data Mining and Computational Intelligence-IADIS, Porto 2019
K. Dzieciolowski, "Explaining Black-box Machine Learning Models"
SAS Global Forum, Denver, 2018
K. Dzieciolowski, "Creating a Successful Data Science Program – A Joint Academic and Industry Perspective"
SAS Global Forum - Orlando, April 2017
K. Dzieciolowski, "Fuzzy Matching and Predictive Models for Acquisition of NewCustomers"
Big Data and Data Mining, JMSB, Concordia, March 2016
European Conference in Data Mining, Las Palmas, Spain, 2015
K. Dzieciolowski, “Predicting Customer Attrition with Markov Chains”
European Conference in Data Mining, Lisbon, Portugal, 2014
K. Dzieciolowski, "Predictive Probabilities in Marketing"
International Association for Development of the Information Society, IADIS,Lisbon, Portugal, 2012
K. Dzieciolowski “Why Data Mining is Difficult to Learn? And What Can We Do to Make It Easier?”
International Association for Development of the Information Society, Amsterdam, Netherlands, 2008
K. Dzieciolowski, D.Kira, “Data Mining in Marketing Acquisition Campaigns”
MITACS Quebec Interchange on Data-Mining (2003), Montreal
K. Dzieciolowski, R. Ducharme, Y. Bengio, A. Lukban, NameMining for Long Distance Calling Pattern Recognition.