Siyuan Liu is with Smeal College of Business, Pennsylvania State University. He received his first Ph.D. degree from Department of Computer Science and Engineering at Hong Kong University of Science and Technology, and the second Ph.D. degree from University of Chinese Academy of Sciences.
Big data for business intelligence, social networks analytics, and mobile marketing.
Ph D, University of Chinese Academy of Sciences, 2014
Ph D, Hong Kong University of Science and Technology, 2011
MIS 301 – Business Analytics (3)
MIS 301 investigates use of databases, basic data mining tools, social networking software, and advanced level of spreadsheet management for analysis of large amounts of data. Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Topics may include methods for analyzing not only structured data, but also unstructured data from the web, emails, blogs, social networks, click streams, etc. Finally, techniques for visualizing, presenting and communicating information in a useful way will be presented.
MIS 494H – Research Project (Variable)
Supervised student activities on research projects identified on an individual or small-group basis.
MIS 441 – Bus Intelligence (3)
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.