In November 2017, Student Party Dante distributed an online survey to evaluate the Master program Data Science: Business and Governance. We made this summary to update you on the results.
Respondents’ background information
21 Data Science students filled in the survey. These students are from various backgrounds. 29% attended the Communication and Information Sciences Bachelor program, 10% attended the Psychology Bachelor program and 10% attended the program Business IT and Management. The other 51% attended various other programs. 8 students attended the Pre-Master’s program, 13 did not.
More than half of the participants think that information is communicated clearly and unambiguously. However, most of the participants say that assignments don’t always have to be handed in always via BlackBoard, but sometimes via e-mail or other websites. A remark by one of the participants was that the teachers of the law course are not familiar with BlackBoard, because they use another system.
Opinions differ about the level of education of the program Data Science: Business and Governance. Critical remarks are, for example:
- There is not a lot of guidance.
- The Pre-Master does not fit the Master program (not the right pre-knowledge required).
- The programming courses are really tough and are taught in an extremely fast way.
- Extremely high working load, many courses simultaneously.
The opinions about the content of the Master program differ as well. Critical remarks are, for example:
- Teachers explain theory, but the practical parts are difficult.
- Some subjects are of a good level, where some of them are a bit too hard.
45% of the respondents think the grades that are granted to assignments are somewhat accommodating and 41% thinks the granted grades are strict. Two subjects were mentioned in particular: Data Processing (quizzes are graded extremely strictly) and Data Mining (grades were adapted to overall results). In contrast, the grades granted to exams are evaluated quite well. 35% of the respondents think the grades were somewhat accommodating and 34% thinks the grades were exactly right.
Most respondents (90%) think theory and practice are well balanced in the study program. However, 48% of the respondents think they are not able to finish their courses in the given time. This includes assignments and readings. Most participants think the courses are very intensive, in particular the practical parts are time consuming.
Most respondents (60%) think the study is innovative. However, an interesting remark was: “Innovative but for some people very beginners level, while for others very challenging and everything is new.” Which is a remark that should be taken into account.
Missing courses, according to respondents:
- Working with Tableau
- Course on implementing findings (Business Strategy)
- Text mining
- Methodological/ontological/epistemological considerations
- Course that explains how Data Science Application Management can be embedded in a website or application
- Course on current developments in the technological industries
Most respondents (58%) don’t think they will be fully skilled Data Scientists when they are graduated. Based on the remarks of respondents, a suggestion for improvement would be making (basic) programming a prerequisite for attending the Master program to make sure courses can get more in-depth explanation, and if students don’t have this basic knowledge it should be possible for them to attend a Pre-Master program in programming. In this way, students should have the same level of knowledge at the beginning of the Master program. Another suggestion for improvement would be to make (it possible to attend) a two-year Master program to get a broader scope. One year could focus on learning skills and the other year could focus on learning and applying advanced skills.
Another point of improvement, according to respondents, is the cooperation of teachers in the different subjects. Some subjects have overlap, where the teachers of other subjects think students already possess certain knowledge they don’t. That includes that it is important that the introduction courses are in the first half year (for example: Introduction to R), prior to the more advanced courses in the second half year (for example: R through Statistics). A last wish of the students is a central hub (person, room, …) to bring together the questions, issues etc. students have.
We as Faculty Council will try and work to improve the program using these findings. If you have any other remarks on the Data Science program that were not included in this report, contact us via email@example.com.