COSC 370

Data & Visualization


Please read this syllabus thoroughly!


Dr. Beau M. Christ

Phone: (864) 597-4528
Office: Olin 204F
Office Hours: MWF from 1:00PM - 3:00PM and TR from 9:30AM - 11:00AM

If you have any questions at all, feel free to contact me by email or phone, or stop by my office during office hours. You can also try to catch me at other times or make an appointment. I am always happy to talk!

Meeting Time & Location

We will meet every Tuesday and Thursday from 1:00PM-2:20PM in Olin 213.

Required Textbook

We will use Data Visualisation with R (1st edition) by Thomas Rahlf.

Course Overview & Goals

Welcome to COSC 370: Data & Visualization!

Data science has been ranked in the past few years by Glassdoor as the best job to have in America. It is a hot area, and the demand for workers in the field is booming. This course explores one of the most important areas of data science: visualization. We will investigate topics from data science, but will focus on how to visualize datasets to demonstrate what is most relevant as to what you are trying to convey. We will also see how visualizing data in different ways often leads to new discoveries about the data.

Topics will include basic data science, the R programming language, visualization techniques (charts, graphs, etc.), problem solving, and interactive visualization using Javascript and D3. The skills you will learn in this course are not only useful if you are studying computer science, but also applicable to a wide range of other disciplines as well (there are many fields that produce and study data).

Prerequisites: COSC 235 (Programming & Problem Solving) with a minimum grade of C.

Catalog Description: An introduction to data and visualization, part of the interdisciplinary field of computational science. The course contains a brief introduction to the network environment and the UNIX operating system. Because large Web-accessible databases are becoming prevalent for storing scientific information, the course covers the concepts and development of distributed relational databases. Effective visualization of data helps scientists extract information and communicate results. Students will learn fundamental concepts, tools, and algorithms of computer graphics and scientific visualization in three dimensions. Throughout, applications in the sciences are emphasized.

Course Goals

By taking this course, my goal is for you to:

  1. Gain a greater understanding of data science including what data is, how it is stored, and how to make sense of it.
  2. Learn basic problem solving skills and how to apply them to data science.
  3. Learn the R programming language, as well as explore a little bit of Javascript.
  4. Explore the creation of graphics, both static images and interactive animations.
  5. Observe popular industry practices and technologies that today's data scientists are using.
  6. Be well prepared for further study in computer science (or any field that uses data).

You will fulfill these objectives by:


All grades will be recorded in Moodle as the semester progresses, including your final grade. Your final grade will be weighted as follows:

Projects (70%)

You will complete multiple projects to obtain hands-on experience with computer programming. These will be completed mostly using the R programming language, and will be submitted via Moodle. Every project will be equally weighted, and each will be given a grade out of 10 points.

Final Project (30%)

You will complete a final project that will review all of the concepts learned throughout the semester, and will present it to the class. This will occur on the scheduled final exam date.

Grading Scale

Grades will be rounded (92.49% = A- and 92.5% = A)

F D C- C- C+- B- B B+ A- A
0% - 59% 60% - 69% 70% - 72% 73% - 76% 77% - 79% 80% - 82% 83% - 86% 87% - 89% 90% - 92% 93% - 100%



You are expected to attend class. I do understand that absences are sometimes unavoidable, so I appreciate an email letting me know in advance that you will be absent. You are responsible for catching up on missed classes. If you do not let me know ahead of time that you will be absent on a quiz day, you will not get a chance to retake the quiz. If you have an excused absence, contact me before the quiz to reschedule taking it.

Finally, in accordance with Wofford policy, you must be present for the final exam.


You are encouraged to bring your computer to work along with the examples in class. I highly advise you, however, to not become distracted by your devices (notebook, phone, tablet, etc.) for things other than course-related use. Not only are you missing out and inhibiting your learning, but it is often a distraction to others as well.


You are expected to keep up with all coursework and due dates during the semester. Submitting coursework past the due date/time (even by a single minute!) will result in a 20% penalty for that particular project. After that, you have 24 hours to submit the late work. After 24 hours, the project will not be accepted under any circumstances and will receive a 0. There are a few reasons that are acceptable (medical, family emergencies, etc.), but I will usually only grant extensions for those cases when receiving an email or phone call before the due date. I will decide on a case-by-case basis, but having official documentation will help make your case.

According to Wofford policy, you must be present during the final exam time.


I will use email for all communication. Feel free to contact me using "".


Please do your own work! I have caught students cheating in the past, and take these matters very seriously. Any student I determine is guilty of academic dishonesty will receive a '0' for the assignment and have their case referred to the department and the college to be pursued further (trust me, you do not want that to happen). You may discuss ideas and pseudocode with other students, but all work must be your own. I will be using software to analyze your code to see if it closely matches that of another student.

To make sure you understand what constitutes academic dishonesty, please read the Wofford Honor Code. By enrolling in this course, you are pledging that you agree to the Wofford Honor Code and that all submitted work is your own. Please talk to me if you are unsure what constitutes academic dishonesty.


If you need accomodations with anything, please contact both the Wofford Accessibility Services and myself at the beginning of the semester.