Admission requirements
This course is open to students from outside the master programme Statistics & Data Science. Students should be very familiar with R, RStudio and be able to generate reproducible reports with RMarkdown. Within the Statistics & Data Science master this pre-knowledge can be acquired from the course ‘Statistical Computing with R' and it is recommended that students pass this course first.
Description
Data visualizations give viewers insight in data patterns and are therefore an integral part of data analysis and the communication of statistical results. Hence, applied statisticians and other researchers should learn how to visualize their data in an effective and perceptually accurate manner such that the plots correctly convey the data message. Additionally, they should be able to visualize the statistical models fitted on data and these models’ uncertainty, so that they can easily be interpreted by users.
In this course you will learn about the principles of effective data visualizations and how to apply them. Furthermore, you will learn how to make explorative data visualizations and visualizations of statistical models, and how to visualize model uncertainty. In this course all plots are made in R (mainly with the ggplot2 package) in a reproducible way (R Markdown).
Course Objectives
By the end of the course, students can:
1. apply the principles of effective data visualizations to make perceptually accurate plots;
2. evaluate data visualizations and describe improvements;
3. create reproducible explorative data visualizations for data explorations, statistical models, and model or data uncertainty;
4. create reproducible visualizations that convey a data story.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of Instruction
The course consists of one course-day per week in the form of a computer practical in which students learn how to make data visualizations with R. Students are expected to prepare for each practical, for example, by reading literature, watching lecture videos, and making preparatory exercises.
Additionally, there are three interactive workgroups in which pairs of students work on a visualization assignment, share it with the group, and give each other feedback. Active participation in these workgroups is mandatory (see assessment method).
Make sure you have a laptop available during each meeting with the latest version of R and R-Studio (details will be provided via Brightspace).
Assessment method
The final grade is based on three components:
20%: Active participation in interactive workgroups
In each interactive workgroup students work on a group assignment which is discussed at the end of the class.
Graded as pass/fail. Students fail if they do not actively participate in more than one workgroup.
Students need to pass this part in order to pass the course.
Resit: there is no resit opportunity for this part. However, students may miss at most one meeting (see previous point).
40%: Theoretical exam
Covers the topics presented in the whole course
Graded on a 0-10 scale, minimum grade = 5.0
Resit: a resit exam can replace the original grade of the exam
40%: Assignment
Assignment is done on campus within a set time (like an exam)
Graded on a 0-10 scale, minimum grade = 5.0
Resit: a resit assignment can replace the original grade of the assignment
To pass the course, the unrounded final grade should be a 5.5 or higher.
Reading list
Book: Fundamentals of Data Visualization by Claus O. Wilke, available online via clauswilke.com/dataviz.
Various scientific articles that will be announced at the start of the course via Brightspace and are available online via the Leiden University Library.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Course coordinator: Dr. Sanne Willems (s.j.w.willems@fsw.leidenuniv.nl)
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.