Full course description
Business Data Mining:
Charting a roadmap for data-driven decisions making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patters from primarily numeric data
At the end of this course, students will be able to:
- Approach business problems and envision opportunities data-analytically. Think carefully and systematically about how data-driven analytics can improve business performance and help make better-informed decisions for management, marketing, investment, etc.
- Be able to interact completely on the topic of data mining and predictive modeling. Know the fundamental principles of data-mining that are the basis for predictive modeling. Know the fundamental principles of data-mining that are the basis for predictive modeling, algorithms, and systems. Understand these well enough to interact with CIOs, data scientists, and consultants.
- Hands-on experience in formulating problems based on data that we will provide, using the various techniques discussed in class, and building and evaluating predictive models.
Unfortunately, federal financial aid is not available for certificate or professional development programs.
Meet the instructors
Xiqing Sha, Clinical Assistant Professor of Information Systems
Xiqing Sha is a clinical assistant professor in Information Systems with the W. P. Carey School of Business at ASU. Her research interests focus on social networks in entrepreneurial teams and online healthcare community. Her research in social networks and healthcare has been published at peer-reviewed journals such as the Journal of the Association for Information Science and Technology, Information, Technology & People, etc. After joining ASU, she focuses on teaching data analytics courses using popular tools such as Python and R. She has actively contributed to the redevelopment of several business data analytics courses.
Sang-Pil Han, Associate Professor of Information Systems
Sang-Pil Han is an associate professor of Information Systems in the W. P. Carey School of Business at the Arizona State University. His research focuses on mobile apps, mobile advertising, and mobile platforms. In his research, he uses econometric analyses, machine learning, structural modeling and randomized field experiments. His papers were published in top-tier journals such as Management Science, Management Information Systems Quarterly, and Information Systems Research.