Learn Data Science at Clemson University
When I began my undergraduate studies in 2010, I had never heard of data science. I’m not sure many of my professors had either. That is a different story today. Understanding mathematics, computers, and data in harmony has become a highly sought after skill. I was fortunate to stumble upon many of the skills required for my work through my degree. Looking back there are many gaps I wished I had filled through my education at Clemson. I hope the following will prevent you from doing the same.
The “Data Science Venn Diagram” shown below is a great summary of the skills required for data science. The topics presented set the foundation for a strong curriculum. The following is a breakdown of the courses offered at Clemson that I believe provide foundational skills for each topic.
Mathematics and Statistics
At the heart of being a data scientist is the ability to build a statistical model. Then you have to be able to communicate that model with the outside world. This is impossible to do without intimately understanding the language of mathematics. The following courses provide the fundamental skills needed to be a productive data scientist.
- MATH 1060 — Calculus I
- MATH 1080 — Calculus II
- MATH 2060 — Calculus III
- MATH 2080 — Differential Equations
- STAT 2300 — Statistical Methods I
- MATH 3110 — Linear Algebra
- MATH 4000 — Theory of Probability
- MATH 4030 — Introduction to Statistical Theory
- MATH 4060 — Sampling Theory and Methods
- MATH 4070 — Regression and Time-Series Analysis
- MATH 4190 — Discrete Mathematics
Hacking Skills
Only in the past few years have we achieved the computational power necessary to facilitate many mathematical algorithms. The following courses will provide you with the fundamental skills required to have a conversation with a computer.
- CPSC 1010 — Computer Science I
- CPSC 1020 — Computer Science II
- CPSC 2070 — Discrete Structures for Computing
- CPSC 2120 — Algorithms and Data Structures
- CPSC 2150 — Software Development Foundations
- CPSC 3520 — Programming Systems
- CPSC 3620 — Distributed and Cluster Computing
- CPSC 4620 — Database Management Systems
If you are interested in computer engineering the following classes can be used a substitutes:
- CPSC 1110 — Introduction to Programming in C [CPSC 1010 and 1020]
- ECE 2220 — Systems Programming [CPSC 2070]
- ECE 2230 — Computer Systems Engineering [CPSC 2120 and 2150]
- ECE 3520 — Programming Systems [CPSC 3520]
- ECE 4730 — Introduction to Parallel Systems [CPSC 3620]
Substantive Expertise
Understanding data and industry is the quintessential part of being a data scientist. Unlike mathematics and programming this can be difficult to do while in school. This isn’t anything to worry about though. Once you begin working you will be forced to learn this.
In the meantime it is worth your time to begin understanding ethics. It is imperative to understand the ethical repercussions of the models you build. Even more so it is important to not make false assumptions about your data. By digging into the following, you will hopefully gain a better understanding of cause and effect.
- PHIL 1030 — Introduction to Ethics
This is optional, but one of the following depending on interest:
- PHIL 3440— Business Ethics
- PHIL 3450 — Environmental Ethics
- PHIL 3460 — Medical Ethics
Next Steps
Currently data science isn’t offered as an official major through the College of Engineering and Science at Clemson. Therefore it may be difficult to take all of the classes listed above without double majoring. Nevertheless once you finish your degree I hope you continue learning. There are many resources out there to help. Leada is a great place to start.
Here is a link to their original post titled “Make Your Own Data Science Major at Cal”
Also, if you are recent Clemson graduate working as a data scientist and have additional recommendations, please let me know. I will add them to the list.
