Scholar Anthony Constantinou On His New And Unorthodox Approach To Teaching Data Analytics


Anthony C. Constantinou is an Assistant Professor in Machine Learning and Data Mining at the Queen Mary University of London. He is also the Head of the Bayesian Artificial Intelligence Research Lab in the same institution. His interests lie in the fields of Probability, Knowledge Engineering, Bayesian Networks, Machine Learning, Game Theory, Artificial Intelligence, and Statistics.


Anthony Constantinou has been a math geek since he was a child and was very sure about the field he wanted to excel in. He has published countless journals and research papers throughout his career. Some of his recent achievements would include such things as a published piece in the Significance magazine entitled, “Things to Know About Bayesian Networks.” He is intrigued by the possibilities involved in a field of Artificial Intelligence. He specializes in the field of Bayesian Network theory and implements concepts like prediction models, risk analysis and uncertainty quantification in real-life problems like agriculture, sports, gambling, project management, forensics, etc.


Anthony teaches basic as well as advanced Data Analytics courses to the undergraduate as well as postgraduate students in Queen Mary University. Constantinou’s teachings mostly focus on methodologies, approaches, innovative techniques and robust tools that help in the process of decision-making in an industry or a business. He believes that exposing the students to industry standard tools and data analysis techniques broadens their horizon and helps them understand the challenges involved in dealing with a larger set of data. He believes understanding a problem is the first step towards devising a solution for it. Click Here to visit his profile on LinkedIn.


These courses also give an overview of the issues involved in the professional, legal, social and ethical aspects of a business that deals with the storage of data and its accurate analysis. The coursework is followed by a rigorous period of in-house practical work or an internship where the students receive a test case and are asked to carry out data analysis, summarize the results and present them for the understanding of the supervisors and the stakeholders.


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