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Perform an Exploratory Data Analysis
Identify patterns in your data using PCA (Principal Component Analysis), a dimensionality reduction technique, and two of the most popular clustering methods: k-means and hierarchical clustering.Medium
10 hours
Interested in this free-access course?
Do you have a large volume of data with multiple variables? Then you need to learn how to perform a multivariate exploratory analysis.
In this course, we will examine ways to efficiently analyze your data using techniques such as Principle Component Analysis, or PCA, which allows you to reduce the number of variables in your dataset while minimizing the amount of information lost.
We will also look at two of the most popular Clustering Methods: the k-means algorithm and hierarchical classification. These enable you to group individuals according to their similarities.
By the end of this course, you will understand when and how to apply these powerful analytical methods to your data.
Learning outcomes
- Carry out a principal component analysis
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1/2
Table of contents
- Part 1
Reduce Dimensions in your Data Using Principal Component Analysis
- Part 2
Find Groups in your Data Using K-Means and Hierarchical Clustering Methods
Contributors
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