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Updated in [October 16th, 2023]
What does this course tell?
(Please note that the following overview content is from the original platform)
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data
Cluster analysis is a powerful toolkit in the data science workbench It is used to find groups of observations (clusters) that share similar characteristics These similarities can inform all kinds of business decisions; for example in marketing it is used to identify distinct groups of customers for which advertisements can be tailored In this course you will learn about two commonly used clustering methods - hierarchical clustering and k-means clustering You wont just learn how to use these methods youll build a
strong intuition
for how they work and how to interpret their results Youll develop this intuition by exploring three different datasets: soccer player positions wholesale customer spending data and longitudinal occupational wage data
We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
During this course on Cluster Analysis in R, the learner will acquire the following skills and knowledge:
1. Understanding of hierarchical clustering: The learner will develop a strong intuition for how hierarchical clustering works. They will learn about different linkage methods (e.g., complete, single, average) and how to interpret the resulting dendrograms.
2. Understanding of k-means clustering: The learner will also develop a strong intuition for how k-means clustering works. They will learn about the initialization methods, convergence criteria, and how to interpret the resulting cluster assignments.
3. Application of clustering methods: The learner will learn how to apply hierarchical clustering and k-means clustering to real-world datasets. They will gain hands-on experience in implementing these methods using R programming language.
4. Interpretation of clustering results: The learner will learn how to interpret the results of clustering analysis. They will understand how to identify meaningful clusters and extract insights from the data.
5. Practical use cases: The learner will explore three different datasets, including soccer player positions, wholesale customer spending data, and longitudinal occupational wage data. They will understand how clustering can be applied in various domains, such as marketing and business decision-making.
Who will benefit from this course?
This course on Cluster Analysis in R will benefit data scientists, analysts, and researchers who want to gain a strong understanding of hierarchical and k-means clustering methods. It is particularly useful for professionals working in marketing, as it can help identify distinct groups of customers for targeted advertising. Additionally, individuals working with large datasets in various industries can benefit from this course, as cluster analysis can provide insights for making informed business decisions.
