What Every Engineer Should Know About Data-Driven Analytics

Satish M. Srinivasan received his B.E. in Information Technology from Bharathidasan University, India and M.S. in Industrial Engineering and Management from the Indian Institute of Technology Kharagpur, India. He earned his Ph.D. in Information Technology from the University of Nebraska at Omaha. Prior to joining Penn State Great Valley, he worked as a postdoctoral research associate at University of Nebraska Medical Center, Omaha. Dr. Srinivasan teaches courses related to database design, data mining, data collection and cleaning, computer, network and web securities, and business process management. His research interests include data aggregation in partially connected networks, fault-tolerance, software engineering, social network analysis, data mining, machine learning, Big Data, and predictive analytics and bioinformatics. Phil Laplante is Professor of Software and Systems Engineering at The Pennsylvania State University. He received his B.S., M.Eng., and Ph.D. from Stevens Institute of Technology and an MBA from the University of Colorado. He is a Fellow of the IEEE and SPIE and has won international awards for his teaching, research, and service. From 2010 to 2017 he led the effort to develop a national licensing exam for software engineers. He has worked in avionics, CAD, and software testing systems and he has published 40 books and more than 300 scholarly papers. He is a licensed professional engineer in the Commonwealth of Pennsylvania. He is also a frequent technology advisor to senior executives, investors, entrepreneurs, and attorneys and actively serves on corporate technology advisory boards. His research interests include artificial intelligent systems, critical systems, requirements engineering, and software quality and management. Prior to his appointment at Penn State he was a software development professional, technology executive, college president, and entrepreneur.

1. Data Collection and Cleaning. 2. Mathematical Background for Predictive Analytics. 3. Introduction to Statistics, Probability, and Information Theory for Analytics. 4. Introduction to Machine Learning. 5. Unsupervised Learning. 6. Supervised Learning. 7. Natural Language Processing for Analyzing Unstructured Data. 8. Predictive Analytics Using Deep Neural Networks. 9. Convolutional Neural Networks (CNN) for Predictive Analytics. 10. Recurrent Neural Networks (RNNs) for Predictive Analytics. 11. Recommender Systems for Predictive Analytics. 12. Architecting Big Data Analytical Pipeline.