Today, the amount of data generated is enormous; smartphones, credit cards, televisions, computers, home appliances, sensors, domotic systems, public and private transport, and so on are just a few examples of devices that generate data seamlessly. Such data is stored and then used for various purposes. One of these is data analysis using machine learning algorithms.

In the previous chapter, we analyzed how to build machine learning models step by step. At the start of the workflow, there is organization of data. Indeed, after collecting data, we typically need to import and preprocess. This first step is crucial for the proper functioning of the model that we will build and then for the final result.

In this chapter, we will have a look at how to import and organize our data in MATLAB. To do this, you should familiarize yourself with the MATLAB workspace in order to make the operations as simple as possible. Then we will analyze the different formats available for the data collected and how to move data into and out of MATLAB.  We will also explore datatypes for working with grouping variables and categorical data and how to export data from the workspace, including cell array, structure array, and tabular data, and save it in a MATLAB-supported file format. Finally we will understand how to organize the data in the correct format for the next phase of data analysis.

We will cover the following topics:

• How to work with the MATLAB workspace
• How to use the MATLAB import tool to select and import data interactively
• The different datatypes supported by MATLAB
• How to export text, images, audio, video, and scientific data from MATLAB
• Discovering different ways to transform data
• Exploring the wide world of MATLAB data
• How to organize your data

At the end of the chapter, we will be able to import, format and organize our data correctly so that we can go on to next step: the exploratory data analysis.