A full course introducing MATLAB for Health Data Science

I have created a full course introducing MATLAB for Data Science. The complete course is on YouTube together with full documentation on GitHub. Let me tell you more...

Python and R are leading computer languages in Data Science. Both languages come with excellent tools for analyzing data and sharing our data-driven stories. These and similar languages are open-source and free to use. They have democratized our ability to find solutions using data.

Open-source languages are not the only tools at our disposal, though. Commercial software and languages such as MATLAB from MathWorks are just as viable for use in Data Science. The fact that they are not free should not deter from their use. In fact, MATLAB is still ubiquitous in many industries and institutions.

MATLAB in particular is a fantastic tool for working with and analyzing data. The language and coding platform is mature, and it shows. Add-on apps and built-in functionality allows even those without coding experience to work with data. Using only buttons and dropdown menus we can import data, analyze the data, create plots and figures, do statistical tests, and create models.

Speaking more than one human language is such a powerful skill. The same goes for computer languages. I personally use Python, R, Julia, the Wolfram Language, and MATLAB. Each of these bring something special to the table.

I produce many similar open educational resources for my students. I never know where their careers might take them. All I can do is give them as many resources as I can, including resources that do not make it into a very full curriculum. They know that I support them long after they have left the classroom. Even if you were never a postgraduate student at the George Washington University, I hope that you can find these resources useful too. Maybe it will even inspire you to join us.

The full video course is available on my second channel, and you can find it by HERE.

The full documentation is available on GitHub, and you can find it by clicking HERE.