Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules (#603)
Issued by
Statistics.com
The course objective is to cover key unsupervised learning techniques including association rules, principal components analysis, and clustering. Students also review integration of supervised and unsupervised learning techniques. Participants apply data mining algorithms to real data, and interpret the results. A final project integrates an unsupervised task with supervised methods covered in predictive Analytics 1 and 2. Students use either R, Excel Solver or Python software.
Additional DetailsSkills
Earning Criteria
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The methods of assessment include quizzes, and more with a minimum passing score of 80%.
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Instructional strategies include: audio visual materials, case studies, discussion, computer-based training, project-based instruction, coaching/mentoring, practical exercises, classroom exercise.
Endorsements
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American Council on Education
This credential has been successfully evaluated by the American Council on Education for college credit. Is is recommended for 3 Upper-Division Baccalaureate college credits in Predictive Analytics. For more information about ACE Learning Evaluations, visit www.acenet.edu. -
American Council on Education
3 semester hours in predictive analytics in the upper-division undergraduate category