A survey of programming languages and techniques to better understand and process data.
Prerequisites: CS 1371
Credit Hours: 3
Average GPA: 3.75
This exploratory course on different software and techniques used for data analysis can be applied in both the research and industry setting. Students will learn how to work with big data in EXCEL, Python, Mathematica, Matlab, R, and C. Each module of the class focuses on one software and what its capabilities are and what it should and shouldn’t be used for, with short independent projects after each module is finished. Classes are formatted to have an open discussion with students guiding the professor on how to approach the daily task for lecture. Whatever part of the task isn’t finished during lecture will be due the next class period as homework. This course often requires students to utilize the internet to complete some of the tasks assigned, in order to properly teach students how to address a problem they might not immediately know the answer to.
To be prepared for this class, one must be able to apply the basics of computer science, basic calculus (think derivatives), and follow along in class.
THE tip: You must follow along in class on your own computer. Often, the homework assignment builds right off of the in-class work. Make sure you form a group chat with a few people or the entire class early on.
Recall: You will utilize Matlab from CS 1371 and statistics from BMED 2400. If you’ve taken BMED 3520, some of the concepts taught in that class can also be applied.
Spend your time… Working on tasks diligently as soon as class ends as you’ll be able to recall what you learned more readily.
Take home? Much of what you’ll take home will help you with other classes that require dealing with data analysis such as 3310 and 3610. A class like this will also help you prepare for a career in bioinformatics, which is a quickly growing field that biomedical engineers can readily take a niche in.