Sound Business Practices for Data Mining and Predictive Analysis
This course addresses the application of data mining and diagnostic analytics to measure business performance. It builds upon these business performance measurements to achieve advanced insights with predictive and perspective analytics. It examines common techniques that can be used to measure the efficacy of machine learning integrations for business processes and reviews a real-world business case study in leveraging machine learning, advanced analytics, and process automation for reducing business operations costs.
What you will learn:
- Applying data mining and diagnostic analytics to enterprise data
- Achieving advanced business insights through predictive and prescriptive modeling
- Evaluating techniques to measure machine learning efficacy on business processes
- Examining a case study for reducing business operations costs with machine learning
This Course is part of the following Course Program:
Machine Learning: Predictive Analysis for Business Decisions
Courses included in this program:
Who should attend: Computer engineers, business executives, industry executives, industry leaders, business leaders, technical managers, data scientists, and data engineers
Grant Scott is an Assistant Professor in the Center for Geospatial Intelligence (CGI) and the Electrical Engineering and Computer Science Department at the University of Missouri.
Throughout his career, Dr. Grant Scott has conducted extensive research on scaling machine learning up for big data. His research focuses on Applied Machine Learning, Computer Vision, as well as Advanced Pattern Analysis, High-dimensional Data Analytics, Advanced Data Systems, and Multi-modal Analytics.
Dr. Scott is a Senior Member of the IEEE Computational Intelligence Society and the IEEE Geoscience Remote Sensing Society.
Publication Year: 2020