Advanced Business Intelligence: Introduction to Predictive Analytics

Advanced Business Intelligence: Introduction to Predictive Analytics

This course introduces the predictive modeling process and basics of predictive analytics for business applications, including hands-on introduction to data preparation, model identification and validation, model documentation, and interpretation of model results.

Topics include:

• Explain predictive analytics key concepts and terms, benefits, and applications
• Understand the steps to creating and selecting a predictive model
• Comprehend the data mining process, including data collection or selection, data cleansing, evaluation of results, best practices and common mistakes
• Explore visualizing and sharing model results
• Understand various predictive analytics models, including: decision trees, regression, cluster analysis, Artificial Neural Networks, and other models
• Understand how predictive analytics is used and lifecycle in a workplace environment

Learning Outcomes:

• Learn about how predictive analytics works, different types of predictive analytics models, and visualizations that can accompany predictive analytics models
• Create visualizations and predictive analytics models that can be used in a portfolio to show potential employers
• Understand how to collect data for either personal projects or to be used in a workplace environment 
• Examine cases of predictive analytics in the real world and ethical issues with cases where predictive analytics are poorly used in the workplace

• Learn important data communication skills for the workplace in addition to how the lifecycle of predictive analytics works in most workplace environments

Hardware/Software Requirements: Anaconda Navigator and Tableau will be used in this course. Both are open source and can be downloaded at no additional cost. 
Note: a computer with an OS of either Windows 10 (released 2015) or newer, or a Mac with an OS of 10.14 (Mohave, released in 2018) or newer is required. A computer with at least 4 GBs of RAM is recommended.

Course typically offered: Online, quarterly

Suggested Prerequisites: CSE-41198 Introduction to statistics using R or previous background knowledge and experience with R or Python and statistics