1.5 EC
Semester 2, period 5, 6
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:
Report absence in advance to the course coordinator: Danielle van Versendaal (d.vanversendaal@uva.nl)
| Item and weight | Details |
|
Final grade | |
|
assignment 1 - data management plan | |
|
2.5 (25%) Data organization & documentation | |
|
2.5 (25%) Data security | |
|
2.5 (25%) Data archival and preservation | |
|
2.5 (25%) Data access | |
|
assignment 2 - your own data story | |
|
2.5 (25%) Accuracy | |
|
2.5 (25%) Clear and compelling message | |
|
2.5 (25%) Aesthetics | |
|
2.5 (25%) Feedback |
Planning (study load: 4-6 hours a week)
There are 4 exercises (for practice), 2 assignments (for a grade) and 4 reflections after each seminar. 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
In the first week, we'll discuss data management, through in-class exercises (e.g. exercise 1.1 ReproHack) you'll find out why this is so important to researchers and organizations. In the second half of our first seminar you'll start working on your own data management plan (assignment 1) using either your own (old) data or a fictional set. This is the first assignment that you'll be graded on. Please familiarize yourself with the rubric. After each seminar there are a couple of reflective questions you'll need to answer (exercise 1.2).
Week 2
Work on your assignment and prepare for the second seminar by reading the texts on data management and open science in the syllabus and complete exercise 2.1a. During the seminar, we'll discuss the principles of open science (exercise 2.1b) and you'll use these insights to improve your research data management plan. In the second half of our second seminar there will be time to get feedback on your assignment. And of course, the reflective questions (exercise 2.2).
Week 3
By the third week, your assignment is due and we expect you to prepare for the third seminar by reading the texts on types of graphs and design principles in the syllabus and completing exercise 3.1a. In this exercise you'll look for examples of data visualizations; one you deem good, one bad and one ugly. These you'll present and discuss in the third seminar. During the third seminar we'll discuss many examples of data visualizations in order to come up with a joint language of effective design principles of data visualization (exercise 3.1b). You'll use these principles to draw up and discuss your own data visualization in the second half of the third seminar (exercise 3.2). This can form the basis of the data story that you'll make for the last assignment. A data story includes visuals and text and it conveys the story you want to tell with your data. Examples of data stories are a scientific poster presentation (Links to an external site.), a corona dashboard (Links to an external site.), an infographic in the newspaper (Links to an external site.) (use your UvA VPN if at home). Reflection afterwards (exercise 3.3).
Week 4
Before the fourth and final seminar you'll have to come up with a first draft to get feedback on from a fellow student in class (exercise 4.1a). And of course, you yourself will review a fellow student's data story and give feedback (exercise 4.1b). Did you get your data story across effectively? In the second half of the seminar there will be time to ask questions and make changes to your data story (assignment 2). Afterwards, you should complete the final reflective questions (exercise 4.2).
Week 5
Your last assignment is due. Also, please fill in the evaluation form as this is the first time we are providing this course and we want to improve on it in the years to come.
The schedule for this course is published on DataNose.