Introduction

Two of MATLAB’s greatest strengths are Analyzing Datasets and Visualizing Data.

In this guide, you’ll learn how to:

  • Import data into MATLAB
  • Perform basic statistical analysis
  • Create simple visualizations

These are foundational skills for working with real-world data in engineering and research contexts.

Reading in Data

Common Data Types

MATLAB can work with a wide variety of data formats, including:

  • Images
  • Text files
  • Spreadsheets (e.g., CSV, Excel)
  • Scientific data formats
  • Audio files
  • JSON and structured data

In this section, we’ll focus on Comma-Separated Values (CSV) files, one of the most commonly used file types.

Reading a CSV File

You can import CSV data using the readtable function:

T = readtable('path/to/your/file.csv');
  • T is stored as a table, which is ideal for working with structured data
  • Make sure the file path is correct relative to your working directory

If you’re unsure how file paths work, feel free to check out our UNIX

Reading Other Data Types

We can use similar logic for reading other types of files

For example, to read an image file:

img = imread('path/to/your/image.jpg');

For very large datasets, MATLAB provides tools such as Datastores that allow you to process data in chunks instead of loading everything into memory at once.

Analyzing Data

MATLAB includes many built-in functions for Statistical Analysis and Exploring Relationships between variables.

Descriptive Statistics

These functions summarize key characteristics of your data:

Function Description
mean Average value
median Middle value
mode Most frequent value
range Difference between max and min
min/max Minimum and maximum values
std Standard deviation

Example:

dataset = [10, 15, 20, 25, 27, 30];

mean_dataset = mean(dataset);

Correlation and Relationships

To measure the relationship between variables, you can use correlation:

x = [2, 4, 5, 6];
y = [1, 3, 5, 6];

corr_value = corr(x', y');
  • Values close to 1 or -1 indicate a strong relationship
  • Values near 0 indicate a weak relationship

Visualizing Data

Visualization helps you explore patterns and communicate results effectively.

Scatter Plots

A simple way to visualize relationships between two variables is with a scatter plot:

x = [2, 4, 5, 6];
y = [1, 3, 5, 6];

scatter(x, y);

Grouped Scatter Plots

If your data includes categories (e.g., different years), you can group points using gscatter:

year = [1998, 1998, 2002, 2002];

gscatter(x, y, year);
  • Each group is displayed in a different color
  • Useful for comparing categories within the same dataset

Next Steps

This guide introduces only a small portion of what MATLAB can do. To continue learning, consider exploring:

  • Advanced plotting and data visualization
  • Working with large datasets and datastores
  • Applying statistical models and machine learning techniques

If you are a student at Colorado State University, be sure to explore MATLAB resources and training opportunities available through the university.

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