CMPSC X425.15 - Introduction To Predictive Analytics
Course Description
This online course covers the primary phases of the development of a predictive analytics model. Participants learn the history of Predictive Analytics and how it relates to solving analytical problems. They learn how to organize the major phases and tasks of model development and the differences between and the specific applications of various machine learning analytical algorithms. Most significantly, participants learn how use the Open Source predictive analytics tool, KNIME, to build a processing flow chart and perform specific analytical operations. This online course is composed of lectures and exercises in the form of PowerPoint presentations. There may be an introductory audio presentation also. The course helps participants understand flow of logic and sequence of operations in the CRISP-DM process model and relate the tasks and sub-tasks of each phase of the CRISP-DM process model to the process of developing a predictive model.
Learner Outcomes
Upon successful completion of this course, students will be able to:
- Outline and apply the predictive analytical process for solving specific problems or answering specific analytical questions
- Perform data integration operations and data cleaning operations
- Perform variable derivation operations, dimension reduction operations, variable selection operation, balancing and partitioning operations, data segmentation operations, modeling operations, and model evaluation operations
- Deploy trained models to predict target variables in new data sets
Notes
This course satisfies the the requirements for I&C SCI X425.61 Introduction to Predictive Analytics offered as part of the Predictive Analytics Certificate Program through UCI Division of Continuing Education. See http://ce.uci.edu/areas/it/