COMSTECH Webinar on Big Data Analytics: A Lucrative Tool for OIC
By Prof. Dr. S. Ejaz Ahmed
Faculty of Math and Science, Brock University, Canada
05th August 2021 (03:00 PM PKT)
Abstract: As data science jargon has begun to flood into government, businesses, and social circles, navigating statistical information and tools can be an arduous and daunting task for policy makers. The rapid growth in the size and scope of data sets in a variety of disciplines have naturally led to the usage of the term, Big Data. The analysis of such data is important in multiple research fields such as engineering, social media networks, bioinformatics, personalized medicine, environmental, neuroscience, astronomy, nanoscience, and financial studies among others. There are many challenging questions. For example, how to acquire, manage, process, analyse, and make sense of big data? Clearly, big data is the future of research in a host disciplines, and trans-disciplinary programs are required to develop the skills for data scientists. For example, many security agencies are using sophisticated number-crunching, data mining, or big data analytics to reveal patterns in information provided by air carriers about passengers.
This presentation will guide the participants from the basics of big data analytics into a deeper discussion of how to handle unconventionally large data matrices in a multitude of applications. For example, genomics data is large and vast accounting for every gene in the body and every gene’s phenotypic expressions. The impact of genomic research for the healthcare industry of a country can be policy changing that benefit OIC.
Model selection, post-estimation, and prediction is imperative for anyone conducting an analysis. As we try to advance government and business practices, being able to predict financial, operational, transactional, etc. information is a lucrative skill. For example, many government-funded initiatives aim to provide appropriate advertisements for programs in their emails. In order to target these customers, the analytics team must collect and analyze the data to monitor consumer behaviour. Based on their customers’ history and demographics, the analytics team can predict and provide an appropriate email for the program.
But a prediction is only as good as its model. Bias in the model can cost a business to make ill-informed decisions. The bias is a systematic partiality that is present in the data collection and modelling process resulting in misleading results. Just how social biases can affect our personal decisions, statistical bias affects analytical modeling. It is important for policymakers and business professionals to understand the analytics done behind the scenes; it allows them to communicate results to key stakeholders with confidence. Implementing data centres that have statistical knowledge can benefit the economic health of an OIC.
Target Audience: This webinar is open to academicians, scientists, and general public from all the member OIC states and beyond.
Profile of the Speaker: Dr. S. Ejaz Ahmed is professor and Dean of the Faculty of Mathematics and Science. He is an internationally known scholar, educator, and an accomplished researcher. His research interests concentrate on big data, predictive modeling, data science, and statistical machine learning with applications in many walks of life. His research has been supported by a variety of grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada since 1987, the Canadian Institute of Health Research, Ontario Centre for Excellence (OCE) and other sources throughout his academic career. Importantly, his NSERC grant was renewed with “Outstanding” in all three categories.
Recently, he was awarded the prestigious Bualuang ASEAN Chair Professorship. His paper entitled “Nonparametric Regression Estimates based on Imputation Techniques for Right-Censored Data,” received Grand Prize Advancement Award by the International Society of Management Science and Engineering Management. The paper was selected by an international awards member team spanning 11 countries. Further, his research achievements have been recognized with honours and awards, including the prestigious status of Fellow of the American Statistical Association, editor/associate editorship to influential scientific journals, adjunct/visiting professorships, and invited scholarly talks around the globe. He is an elected member of the International Statistical Institute and a Fellow of the Royal Statistical Society. He served as a Board of Director and Chairman of the Education Committee of the Statistical Society of Canada. Ahmed authored several books and edited/co-edited several volumes and special issues of scientific journals. Ahmed has been the Technometrics Review Editor for the past ten years. Ahmed was a member of the Board of Directors of the Statistical Society of Canada and Chair of its Education Committee and also the Vice President of Communications for the International Society for Business and Industrial Statistics and member of the “Discovery Grants Evaluation Group” and the “Grant Selection Committee” of the Natural Sciences and Engineering Research Council of Canada.
He published more than 200 research articles in scientific journals and reviewed more than 100 books and travelled the globe doing 200 scholarly presentation. He supervised/co-supervised about 20 Ph.D. students and numerous M.Sc. students, and currently co-supervising 9 Ph.D. students.
Poster of webinar: Attached
Registration Link: https://forms.gle/vfk3PCZmrykorY9v5
Procedure to join:
Zoom link for participation will be shared selected participants.
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