Learn how to take data (consumers, genes, stores, …) and organise them into homogeneous groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks. Learn about this very active field of research in statistics and data mining, and discover new techniques.
Course Outline
The recommended duration for this course is 3 online session(s).
The most popular version of this course comprises 3 sessions described as follows:
Session 1: Principles & Classic Methods- Introduction to Cluster Analysis
- Context of Use, Objective, Terminology
- Principle of Hierarchical Methods: Determining the Distance Between Objects, Linking Clusters (Agglomerative Methods)
- Modeling Techniques
- Optimization Methods
- Other Methods: Fuzzy Clustering
- Dealing with Continuous & Categorical Classification Variables
- Use and Interpretation of Clusters
- Unsupervised Learning
- Software Packages for Cluster Analysis
- Summary
This course was excellent value for the money. Well-structured and with plenty of hands-on opportunities, it is suited to both beginners and to those with some experience in the technique. The instructors were familiar with all the software packages used by the students and were able to offer practical advice on getting the desired output. A very practical course; loaded with information I could put to use right away. Highly recommended.
This course is very well structured and instructed. I attended both the PCA and cluster analysis session followed by workshop. The instructor (Natalie) is very knowledgeable and very good at explaining difficult statistical problem in a simple way. This course is especially suitable for non-statistician who needs to perform hands on data analysis. This course also exposed students to many different popular statistics packages so you can get a flavor of each of them which helps me a lot in choosing tools in my future research.