Exploratory Data Analysis

This repository contains notes and examples on exploratory data analysis from an internal MathWorks seminar series.

Topics

• Principal component analysis
• Outlier analysis and anomaly detection
• Techniques for handling missing data
• Further linear methods for dimensionality reduction
• Multidimensional scaling and t-SNE
• Hierarchical clustering and MANOVA
• k-means and spectral clustering
• Probability distributions and Monte Carlo simulation

Installation and Getting Started

The examples are provided in a MATLAB project.

1. Double-click on the project archive (ExploratoryDataAnalysis.mlproj) to extract it using MATLAB.
2. With MATLAB open, navigate to the newly-created project folder and double-click on the project file (ExploratoryDataAnalysis.prj) to open the project.
3. The example files are provided as Live Scripts (Seminar01_PCA.m etc) within the project.

MathWorks Product Requirements

This toolbox requires MATLAB release R2025a or later.

• MATLAB
• Statistics and Machine Learning Toolbox
• Deep Learning Toolbox
• Bioinformatics Toolbox

License

The license is available in the LICENSE.txt file in this GitHub repository.

Copyright 2025 The MathWorks, Inc.