Applied Data Science
Description: This course explores the rapidly developing field of Data Science in the context of its pragmatic applications. Modern enterprise is a complex system spanning a variety of functions in pursuit of a range of convoluted objectives. Its environment is exposed to the effects of globalization and “era of information”, producing the influx of large amounts of complex multisource data that may contain useful evidence. As a result, present-day decision makers face a truly formidable task of internalizing huge amounts of time-critical information while being expected to always make the right decisions at the right times. Conveniently, Data Science comes to their rescue. Applied Data Science aims to achieve two main goals. The first is to optimize the efficiency of decision making by human managers. The second is to maximize the utilization of available data, so that no important clue is ever missed. This course aims at building expertize required to achieve those goals in practice. Students will have the opportunity to gain and solidify knowledge of the most important contemporary methods of Data Science, and to develop understanding of practical applicability of the studied topics in business scenarios. They will be able learn how to formulate analytic tasks in support of business objectives, how to define successful analytic projects, and how to evaluate utility of existing and potential applications of the discussed technologies in practice. The instructor is a scientist and a practitioner. He has been involved in research towards various topics of machine intelligence and its applications for over two and half decades. He has been a technical lead and executive in the new technology industry. Currently Dr. Dubrawski is faculty at the CMU Robotics Institute where he directs the Auton Lab and leads multiple Applied Data Science projects in support of industry, government, and non-governmental organizations.