3 EC
Semester 1 & 2, period 2, 5
5224PSDM3Y
This course is part of the Professional Skills learning trajectory and it focuses on the soft skills around data handling and has two parts: data management and data visualisation.
Data management
These days, proper and safe data management is important in many professions and a data management plan can help with that. In this course you will learn about the principles and best practices in order to be able to develop or adjust your own data management plan and put it in the context of open science.
Data visualisation
Effective and accurate data visualisation, independent of the technical details, is often not explicitly taught and with the same data set different stories can be told. In this course you will learn about the design principles (the good, the bad and the ugly) that can be used and make better informed choices when visualising data.
Course materials on Canvas
Before the start of the course you're expected to download some software and get your laptop ready, we'll explain how in an announcement on Canvas.
During the lecture we'll first discuss the setup of this course, we'll get to know each other through some data and finally we'll hear from our guest lecturer how data is managed at a big organisation such as the Central Bureau of Statistics (CBS) and finally we'll get into Statistical Disclosure Control.
In the first two seminars you'll learn about Data Management and you'll practice with making your own Data Management Plan (DMP), which is also the first assignment you'll be assessed on. There is a Q&A after the second seminar to get feedback on your DMP or ask any other questions.
In the second half of the course you'll get a feel for Data Visualization and you'll practice with making a Data Story yourself. Some examples of Data Stories are a scientific poster presentation, an infographic in the newspaper, a graphical abstract, or data model. After the fourth and last seminar there is another Q&A for feedback and questions.
During this course, you're expected to spend ~8 hours of self study a week. Additionally, you will spend 4–6 per project (Data Management Plan and Data Story).
|
Activity |
Hours |
|
|
Hoorcollege |
2 |
|
|
Werkcollege |
16 |
|
|
Self study |
52 |
|
|
Provide peer feedback |
4 |
|
|
Project work |
10 |
|
|
Total |
84 |
(3 EC x 28 uur) |
Additional requirements for this course:
All sessions take place on campus; we do not offer hybrid sessions.
To meet the course objectives, participation in group discussions, peer feedback and student interaction is essential. Active participation during all four sessions is therefore mandatory. If you are unable to attend a session, contact the course coordinator prior to the session to discuss your options for passing the course. Missing more than one session will automatically result in a negative assessment (fail; “NAV”) of the course.
By being present and actively participating in group discussions and class assignments you are contributing to the learning environment as well as the learning process of yourself and others. The in class assignments and discussions enable and promote the achievement of the course learning objectives.
Report absence in advance to the course coordinator: Boris W. Berkhout (b.w.berkhout@uva.nl).
| Item and weight | Details |
|
Final grade | |
|
Assignment 1 – Data management plan | Must be ≥ 5.5, NAP if missing |
|
Assignment 2 – Your data story | Must be ≥ 5.5, NAP if missing |
|
Assignment 3 – Reflection: Learning process | Must be ≥ 5.5, NAP if missing |
There are two marked assignments and a reflection exercise. All three need to be passed to pass the course. Assignments handed in after the deadline will not be graded and will need to be resubmitted for the resit.
Some in class or homework exercises are marked with complete/incomplete. All these need to be 'complete' to pass the course.
Final grades are a Pass or Fail.
Assessment of the marked assignments is done using rubrics.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
This course is part of the Professional Skills learning trajectory.
GenAI in Professional Skills
Artificial intelligence (AI), and specifically GenAI, tools are becoming commonplace in society. Although the tools are easy to access, using them effectively requires advanced skill levels. We think AI can be a useful tool, but can also hinder learning by offering ‘shortcuts’ rather than stimulating active learning.
Therefore, we believe that it is important to first learn skills such as writing, critical thinking, and self-reflection before leveraging AI tools to support these processes. We designed our exercises and assignments to be done without AI to achieve the best learning outcomes. During our courses we focus on the skills themselves, not on AI use. Furthermore, using AI requires additional, AI specific, skills as well.
We expect you to not use AI for Professional Skills courses, as these will likely subtract from your learning. If you believe you have a valid use case for (Gen)AI during one of the Professional Skills courses, please discuss this with your teacher first. Note that the use of (Gen)AI for exercises or assignments without the explicit consent of the teacher may result in referral to the exam committee.