Dr. Reneta Barneva, Professor in Applied Professional Studies
Reneta Barneva, professor and chair of the Department of Applied Professional Studies in the School of Business, delivered a talk that explored ethical and legal aspects of big data analytics at UP STAT 2018, the seventh annual conference of the Upstate Chapters of the American Statistical Organization, held April 20 and 21 at the University of Rochester.
Experts throughout Western New York attended the event, whose theme was “Better Living Through Statistics,” to discuss how big data analytics can be applied in a variety of fields, such as health, security, sustainability, science and education. Drs. Nancy Boynton from the Department of Mathematical Sciences and Dani McMay from the Department of Psychology were also among conference participants.
While scientists are excited by new opportunities, they are also concerned by some ethical questions and issues that data analytics poses, but which are often overlooked. In her talk, Dr. Barneva considered a number of case studies to illustrate the concerns and reviewed the steps taken to solve emerging issues. Some faculty at Fredonia already work in the field of data science and teach courses on data analytics or include elements of data science in their instruction.
Barneva was invited to speak by general conference chair Ernest Fokoué, associate professor of Mathematical Sciences at Rochester Institute of Technology.
Among the 200 attendees were students who participated a data competition and presented posters.
They also had the opportunity to work on real-life data supplied by the Rochester City Police Department and solve practical problems.
Ed Lazowska, founding chair of the Computing Community Consortium and the Bill & Melinda Gates Chair in Computer Science and Engineering at the University of Washington, believes there is a need for specialists in all fields possessing the skills of applying data analytics in their respective areas to obtain knowledge.
The conference’s goal was to promote advances in data science – a new interdisciplinary field quickly gaining popularity that is expected to be a driving force in life and science in the future. It utilizes methods and models from mathematics, statistics, information science and computer science and applies them in social and natural sciences, business, medicine, security, sports, humanities and education to extract information from data.
