index.md

title	author	date	number-sections
Multivariate analysis	Martin van Rongen	today	false

Overview

This course provides practical materials and background information on the analysis of multivariate data.

::: {.callout-tip}

Learning Objectives

After this course participants should be able to:

• perform Principal Component Analysis (PCA)
• perform K-means clustering
• perform basic hierarchical clustering

:::

Target Audience

Participants are generally those who need to analyse data that contain many different variables. They can be graduate or postgraduate students, or researchers who find themselves having to do this.

Prerequisites

Participants should have some knowledge regarding statistics. Attending the Core statistics course is recommended.

More importantly, participants should have a working knowledge of either R or Python, since the topics that are covered require data wrangling and visualisation.

Exercises

Exercises in these materials are labelled according to their level of difficulty:

Level	Description
{{< fa solid star >}} {{< fa regular star >}} {{< fa regular star >}}	Exercises in level 1 are simpler and designed to get you familiar with the concepts and syntax covered in the course.
{{< fa solid star >}} {{< fa solid star >}} {{< fa regular star >}}	Exercises in level 2 combine different concepts together and apply it to a given task.
{{< fa solid star >}} {{< fa solid star >}} {{< fa solid star >}}	Exercises in level 3 require going beyond the concepts and syntax introduced to solve new problems.

Authors

About the authors:

• Martin van Rongen
  Affiliation: Bioinformatics Training Facility, University of Cambridge
  Roles: writing - original draft, review & editing; conceptualisation; coding