CSC 234
Data Visualization


When we meet: TTh 9:00-10:45
Where we meet: Wold 128
Instructor: Prof. Chris Fernandes
Office Hours: TBA or anytime my door's open!
Office: 208 Steinmetz Hall
Phone: 388-6401
Course Webpage:


Course Summary

We live in an age where there is a lot of data being produced. Whether it is mapping the human genome, building a corpus of words from classical Greek texts, or recording each and every purchase from a department store, there is a lot of data being stored. The fields of engineering, medicine, business, the social sciences, the sciences, and the humanities are all using large sets of data to do research, find patterns, and make decisions.

This course is about using visual representations to make sense out of all that data. Good visualizations present a specific interpretation of data which can ultimately improve comprehension, communication, and decision making.

By the end of the course, you should be proficient in the following:

Prerequisite: C- or better in CSC-10X, or its equivalent. Or permission of the instructor. You do NOT need to know Python in advance, even though most of your programs will be in that language. If you are a veteran of some programming language (and you all are), you'll be able to pick up the syntax using our online book above in short order.

What is this course NOT?

For many of you, your experience with CS has been just a programming course or two. But there's a lot more to CS than just programming, and so this course might seem quite different to you. It'll be easier at the outset knowing that this course is


If you plan on using your own computer for projects, go to the Software section on our Nexus page and install all of the packages there. Do NOT wait until the first assignment is out to do this -- installing some of these packages can be a task in itself. Alternatively, you can use our CS lab iMacs, which already has these installed. We have three lab spaces:

All of these labs are available to you 24/7 using your ID card, except when classes are being held in them.



Academic Dishonesty

We have an honorcode now and I trust y'all to follow it. Read up on it at All suspected violations will be reported to the Honor Council chair and Dean of Studies. You must include an abridged honor code affirmation in the comments or prose of everything you hand in.

Here are some concrete things to avoid (this is not a complete list):

Ok, so what should you do? Here are some tips:

It is ok to reuse code...

It is NOT ok to reuse code...

Here's the bottom line: except for the above, you have to write all the code yourself, from scratch. In all cases, you must explicitly cite any source (like a web page tutorial or a helpdesk person) that you use to help complete an assignment. Again, this is similar to writing an English paper; if you use a quote or material from someone else, you have to give credit where credit is due. Otherwise you are inappropriately plagiarizing or borrowing ideas. You do not have to cite help from me.

What you need to do

To prepare for class, you are required to do the following:

The Bottom Line

Ask questions and seek help. This is the most important point of all. I live to answer questions. Don't be afraid to come to my office every single day if you want. It's better for everybody (you AND me) if you understand things sooner rather than later. More often than not, there's a line of people waiting to see me on the day before a project is due. You'll get the help you need faster by starting on projects sooner rather than waiting until the last minute.

Any student with a documented learning disorder is welcome to come talk to me privately about options for completion of exams and homework assignments.