Concordia University

http://www.concordia.ca/content/concordia/en/jmsb/faculty.html

Krzysztof Dzieciolowski, PhD

Part-time Faculty, Supply Chain and Business Technology Management
Industry Advisory Board to Data Science Research Centre, Computer Science and Software Engineering

Office: S-MB 12363 
John Molson Building,
1450 Guy
Phone: (514) 848-2424 ext. 2989
Email: krzysztof.dzieciolowski@concordia.ca

Biography

Dr. Dzieciolowski’s career combines 20  years in data science 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.

 

He currently leads a Modeling and Analytics group in Rogers Communications where he develops predictive models and analytical solutions in key business areas of managing the business value, customer retention, revenue growth and acquisition of new customers.

 

At JMSB, Dr. Dzieciolowski teaches courses in data mining,statistics and analytical software to undergraduate and graduate students. He has built synergies between industry and academia and funded scholarships for students of Montreal universities. He has been instrumental in launching in 2016 a joint Certification in Business Analytics between JMSB and SAS – the world’s leader in data science software.

 

Dr. Dzieciolowski’s research interests focus on statistics and data science methods; in particular, on big data, predictive models, multivariate analysis, forecasting, econometric models, social networks and text analytics.In 2016 he obtained CUPFA Special JMSB Research award for the project in big data.

 

He obtained master’s degree 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 professional gatherings. 


Teaching activities

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.


Research activities

BRIDGING ACADEMIC AND INDUSTRY EXPERTISE FOR BIG DATA PROBABILISTIC DATA MATCHING - CUPFA grant for special project in Big Data

https://www.concordia.ca/jmsb/about/departments/jmsb/about/departments/supply-chain-business-technology-management/cunews/main/stories/2016/03/09/jmsb-lecturer-krzysztof-dzieciolowski-wins-cupfa-special-project-award.html


Publications

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

        http://www.cirano.qc.ca/pdf/publication/2004RP-21.pdf

K. Dzieciolowski, W.H. Ross, (1990), Assessing Case Influence on Confidence Intervals in Nonlinear Regression, Canadian Journal of Statistics, v18, 2, 127-139


Conferences and presentations

Big Data and Data Mining, JMSB, Concordia, March 2016

http://www.concordia.ca/content/shared/en/events/jmsb/2016/03/15/big-data-and-data-mining.html

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.

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