Unsupervised Multivariate Methods
Data reduction is a key process in business analytics projects. In this module, learners will learn data reduction methods such as Principal Component Analysis, Factor Analysis and Multidimensional Scaling.
Students will develop skills related to the formation of segments using cluster analysis methods. Additionally, students will analyse segments, the process of which is a key technique for large groups of data as intrinsic information appears in detail once segmented thoughtfully.
Required Reading Material:
- Applied Unsupervised Learning with R Publisher: Packt R Copyright Year: March 2019 ISBN 9781789956399 Alok Malik, Bradford Tuckfield
- Accreditation: Swiss Private Course
- Total workload: 150 hours
- Requires extra purchases (outside texts, etc.): Yes, purchases required
- ID verification: Required
- Admission requirements: Application required
- Minimum education requirement for students: Undergraduate
Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.
English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.