Utility Analytics 101
Utility Analytics 101 is creating better citizen data scientists. This training is an introduction to utility analytics. You will learn about utility analytics terms and relationships, and about the world of data, including big data, databases, data structures, and data types. You will benefit from learning about utility analytics uses cases in various focus areas like asset health analytics, customer analytics, grid analytics, and safety analytics. You will explore data analysis and data prep with SQL. There is an introduction to and demonstrations of the fundamental concepts and best practices of data visualizations and you will learn how best to communicate results from your data analysis. Lastly, you will get time for application work and you will put it all together with Python.
Charles NicholsonAssociate Professor, School of Industrial and Systems Engineering
Professor Charles D. Nicholson is an Associate Professor in the School of Industrial and Systems Engineering in the Gallogly College of Engineering at the University of Oklahoma. His research focuses on operations research/management, science mathematical programming, metaheuristic and evolutionary search, network flows, and applications of data mining. received his doctorate in Operations Research from Southern Methodist University in Dallas, TX specializing in optimization of network flow problems. He also holds a Master of Science in Decision Technology and undergraduate degrees in Mathematics and Physics from the University of North Texas. He joined the faculty of the School of Industrial and Systems Engineering at the University of Oklahoma in 2013 where his research and teaching emphasis is in the field of Analytics. Prior to accepting the faculty appointment, Dr. Nicholson was the director of analytics for a multi-billion dollar company and later, the founder of his own analytics consulting company. His professional background includes 10 years of experience working as a leader in predictive analytics. He has real-world experience working with large, complex data and collaborating with cross-functional teams to discover and deploy strategic, data-driven insights. His portfolio of projects includes data-mining and predictive modeling, simulation and optimization, and business intelligence with applications in marketing, finance, Geographic Information Systems, product allocation, and operations. Currently, Dr. Nicholson’s research focus is in the development and application of statistical and machine learning algorithms that improve the speed, efficiency, and quality of insight from analytics on large data systems.
Dean F. HougenAssociate Director & Associate Professor, OSU School of Computer Science
Professor Dean F. Hougen is the Associate Director of and an Associate Professor in the School of Computer Science, a member of the faculty steering committee for the Data Science and Analytics program, and a member of the graduate faculty in the School of Electrical and Computer Engineering, all in the Gallogly College of Engineering at the University of Oklahoma. Dr. Hougen has a PhD in Computer Science and Engineering from the University of Minnesota, with a graduate minor in Cognitive Science, and a BS in Computer Science from Iowa State University with minors in Philosophy and Mathematics. His primary research involves robotics and machine learning, focusing on distributed, heterogeneous, multi- agent robotic systems and situated learning in real robotic systems, including reinforcement learning, connectionist learning, and evolutionary computation. He has also worked in the areas of expert systems, decision support systems, geographic information systems, mobile software, and user interfaces. Dr. Hougen has collaborated on grant and contract awards of nearly $7M since coming to OU in 2001 and has authored more than 100 refereed publications in the areas of artificial intelligence, machine learning, robotics, data compression, computer science and engineering education, ethics, and others during his career. He has a thirty-year history of developing fielded software and hardware systems including OU’s first official iPhone application OU2GO in Summer 2009.
Sridhar Radhakrishnan Co-Director, Data Science and Analytics Institute Interim Associate Dean for Partnerships, Gallogly College of EngineeringSridhar RadhakrishnanCo-Director, Data Science and Analytics Institute Interim Associate Dean for Partnerships, Gallogly College of Engineering
Sridhar Radhakrishnan has been a faculty member at OU since the fall of 1990. He completed his Ph.D. in computer science from Louisiana State University and earned an MS in Systems Science and an M.L.I.S degree. He completed his B.Sc in Physics from Vivekananda College, University of Madras, India, and a B.S. in Computer Information Systems from the University of South Alabama, Mobile, Alabama. His research areas include graph algorithms, high performance computing, computer networks, and computational finance. He has published numerous research monographs and a textbook on Data Structures. He has been a PI and a co-PI on many research grants from state and federal agencies, and industry. He is a member of the ACM and a senior member of the IEEE. He currently serves as one of the commissioners for the computing accreditation commission of ABET.
Talayeh RazzaghiAssistant Professor, School of Industrial and Systems Engineering
Professor Talayeh Razzaghi is an assistant professor in the School of Industrial and Systems Engineering at the University of Oklahoma. She received her PhD in industrial engineering from the University of Central Florida, and her Master’s degree in industrial engineering from Sharif University of Technology in Iran. Upon graduation, Dr. Razzaghi worked served as a postdoctoral research associate at Clemson University’s School of Computing. While at Clemson University, she also jointly served as an embedded scholar at the Greenville Health System. Prior to joining OU, Dr. Razzaghi worked as an assistant professor at the New Mexico State University’s Industrial Engineering department. Her research program at OU focuses on the development and use of data-driven analytical models to guide decision making for real-world problems, particularly energy analytics, smart manufacturing, and healthcare informatics. In her research, she primarily employs the theory of machine learning and data mining for the settings with the presence of imperfect, noisy, and possibly massive datasets.