Admission requirements
Students should be very familiar with R, RStudio and be able to generate reproducible reports with RMarkdown. Within this master this pre-knowledge can be acquired from the course ‘Statistical Computing with R' and it is recommended that students pass this course first.
Note: in 2022-2023 this course is only open for students of the master programme Statistics & Data Science.
Description
Data visualizations give viewers insight in data patterns and are therefore an integral part of data analysis. Hence, applied statisticians should learn how to visualize their data in an effective manner. Additionally, statisticians 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:
know the principles of effective data visualizations and can apply them;
can evaluate data visualizations and list improvements;
can make reproducible explorative data visualizations;
can make reproducible data visualizations of statistical models and model uncertainty.
Timetable
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of Instruction
The course will consist of a combination of lectures, computer practicals and self-study. Additionally, there are three interactive workgroups in which groups of students work on a visualization assignment, share it with the group, and give each other peer feedback. Active participation in these workgroups is mandatory (see assessment method).
Assessment method
The final grade is based on three components:
20%: Active participation
40%: Theoretical exam:
Covers the topics presented in the whole course
Graded on a 0-10 scale, minimum grade = 5.0
40%: Visualization assignment
Assignment is done on campus within a set time (like an exam)
Graded on a 0-10 scale, minimum grade = 5.0
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.
Links to additional literature will be provided via Brightspace.
Registration
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Contact
Dr. S.J.W. Willems: s.j.w.willems[at]fsw.leidenuniv.nl