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.
- Explain predicative analytics key concepts and terms, benefits, and applications
- Identify and set up the business problem for predictive analytics
- Understand the steps to creating 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
- Perform various data mining techniques, including decision trees, regression, cluster analysis, Artificual Neural Networks, and various ensemble methods
- Examine case studies of successful data mining applications in business, industry, and science
Software: R Programming and Weka 3 will be used in this course. Both are open source and can be downloaded at no additional cost.
Course typically offered: Online in Fall and Spring
Prerequisites: CSE-41198: Introduction to Statistics Using R or previous background knowledge and experience with a programming language and statistics.
Next step: Upon completion of this course, consider taking Fundamentals of Data Mining for further learning.
More information: For more information about this course, please contact email@example.com.
Course Number: CSE-41288
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence Analysis, R for Data Analytics