Pozivamo Vas na 7. fakultetski seminar u srijedu 29.03.2017. u amfiteatru 3F-A-0-1 u 12:00 sati na kojem će Dr. Timothy Robinson održati predavanje pod naslovom „Data Science: Efficiently Mobilizing Data for Decision Making”
Recently, IBM reported that 90% of all of the data in the world has been produced in the last two years. The explosion in the prevalence of data has the potential to fundamentally change the way we live our lives – how we provide healthcare, how we do business, how we educate students, etc. While the existence of big data offers great potential for improving different facets of life, the inability to mobilize the data often renders it useless for this purpose. The workflow process required to efficiently mobilize data requires careful thought, planning and budgeting. In 2014, Gartner Research estimated that 64% of all large business enterprises worldwide planned to implement big data projects but it was also estimated that 85% of all Fortune 500 companies would be unsuccessful in implementing these projects due to workflow process inefficiencies. The science involved in creating an efficient data workflow process is known as ‘data science’. This talk presents a case study in data science as it applies to conservation management. I also discuss the implementation of data science tools as they relate to a variety of other scientific disciplines.
Dr. Robinson received his PhD in Statistics from Virginia Tech. He is a Professor of Statistics at the University of Wyoming and is the Director of the WWAMI Medical Education Program at the University of Wyoming. He is a Fellow of the American Statistical Association and the American Society of Quality. He serves as an Associate Editor for Quality Engineering, Journal of Statistics Education and Quality and Reliability Engineering International. Dr. Robinson is currently on sabbatical where he was recently an Erskine Visitor at the University of Canterbury in Christchurch, New Zealand. While in New Zealand, Dr. Robinson worked with the University of Canterbury School of Mathematics and Statistics in helping develop their data science program.