1.5 EC
Semester 1 & 2, period 2, 5
5224PSDM2Y
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 visualization.
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 visualization
Effective and accurate data visualization, independent of the technical details, is often not explicitly thought 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 visualizing data.
Git and GitHub Desktop
PowerBI
Tableau
Excel
Or any other programme you feel comfortable with
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, a corona dashboard, an infographic in the newspaper, a graphical abstractLinks to an external site., or data modelLinks to an external site. After the fourth and last seminar there is another Q&A for feedback and questions.
During this course, you're expected to spend ~4-6 hours of self study a week.
|
Activity |
Hours |
|
|
Lecture |
2 |
|
|
Seminars |
6 |
|
|
Self study |
34 |
|
|
Total |
42 |
(1.5 EC x 28 uur) |
Requirements of the programme concerning attendance (OER-B):
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 result in a negative assessment (fail; “NAV”) of the course.
| Item and weight | Details |
|
Final grade |
Planning (study load: 4-6 hours a week)
There are 4 exercises (for practice), 3 assignments (for a grade), including one reflection. Please note there are weeks in between when there is no class due to holidays (see datanose.nl for the schedule).
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
Week 1 & 2
The first two week we will focus on data management and you will make a data management plan.
Week 3
Hand in your data management plan.
Week 4 & 5
During this part we will look into data visualisation principles. Using these you will produce a data story.
Week 6
Hand in your data story.
Week 7
Hand in your reflection