Frequently Asked Questions

Below we list several of the most common questions (and answers) concerning the course:

Q: How about practical sessions?

A: There are no practical sessions, as students are supposed to work on assignments which can use datasets and techniques/designs of their own choice. Practical information on the usage of Visual Analytics tools, such as Tableau, is provided globally during the main lectures.

Q: What should I deliver in the end? How do I get my grade?

A: See the detailed descriptions of the two assignments in the left sidebar.

Q: When is the end? When should I deliver my stuff?

A: The end coincides with the live presentation of your work in the 10-minute session. The date should be visible in your online study calendar (Ocasys and/or Nestor). The written reports and other mandated deliverables are to be delivered before or latest with the occasion of this oral presentation.

Q: Do I have to complete all the steps of both assignments?

A: Yes. Completing less means you will have a lesser grade. Depending on how much (or how little) of the steps you complete, you will get a higher (or lower) grade, which implies you may pass (or not) the course.

Q: Do I have to complete the assignment steps in the indicated order?

A:Yes. Completing them in another order makes it harder for your teacher to provide you with feedback and follow your progress. It is also not logical, as several steps depend on each other in this order.

Q: How do I know if I completed a step successfully?

A: Have the implemented material shown to your teacher, and ask him. He will tell if your work is sufficient or, if not, what needs improvement.

Q: What if I work more on one step, e.g. adding extra features?

A: This is very good! It will only increase your knowledge, help you by the following steps, and surely increase your grade.

Q: Do I have to use Tableau to complete the practical assignment? I am more interested in developing/programming my own Visual Analytics system!

A: The aim of the practical assignment is to let you learn how to create a wide range of information visualizations that work efficiently and effectively on real world big data. Doing this by developing such visualizations from scratch would be possible, in theory, but would take a very large amount of time, out of the scope of this course. It would also require extensive background in graphics programming, database programming, data mining, and interaction design. Separately, there are few (9if any) really comprehensive infovis toolkits that cover most of the techniques discussed in the course, that you could build your own programming on.

However, if you are (very) good in programming, have a (very) good background in interactive computer graphics, and you are interested in infovis research, it is possible to design a custom programming-intensive research-oriented assignment to do instead of the standard assignments proposed for this course. Please directly contact the lecturer in this case.