SHA577 - Clustering, Classification, and Machine Learning in R

Course Description

When faced with a large volume of unstructured data, the question quickly arises: what does this all mean? Techniques in machine learning offer the promise of a meaningful answer to that question. Unsupervised machine learning is a powerful tool that is being put to use in many disciplines. In this course, you’ll experience machine learning through scripting in the statistical programming language R.

The course focuses on using unsupervised machine learning to bring coherence to unstructured data. Specifically, you’ll use different methods to generate clusters within your data set when no dependent variable is specified. Using supervised machine learning approaches, you’ll build and evaluate models that allow you to classify your data and understand the marginal impacts of each attribute. And you’ll gain experience with powerful tools in R that allow you to efficiently evaluate competing models to find the one that gives you the most accurate results.

You are required to have completed the following course or have equivalent experience before taking this course:

• Predictive Analytics in R

Faculty Author

Benefits to the Learner

• Create clusters for structured data with no specified dependent variable
• Choose appropriate models and methods for classifying items in a set
• Apply and compare many solution methods simultaneously

Target Audience

• Analysts
• Developers
• Data scientists
• Functional managers
• Consultants
• Any professional that uses data to make business decisions

Accrediting Associations

• International Institute of Business Analysis (IIBA) 10.0 Hours