Admission requirements
This course is obligatory for students of the MSc Governance of Sustainability.
To participate in Quantitative Research skills, students need to have completed at least 48 EC of the first year MSc courses. For this course, we assume a basic knowledge and understanding of statistical methods and theory. Such knowledge and understanding are assumed to be captured by the general prerequisite of the MSc Governance of Sustainability programme of 8EC quantitative skills. An elaboration of the requirements regarding prior (statistical) knowledge is provided in an on-line video on Brightspace. We expect all (prospective) students to have watched and acted upon the video prior to the course. It is also assumed that all students have (R and) Rstudio installed on their private devices prior to the course.
If you have no prior experience in R then we strongly advise you to complete an online course before the start of QRS in September such as those provided by Code Academy, udemy, or Rstudio (links provided on Brightspace).
Description
Humanity is currently experiencing the data revolution. This revolution represents a unique opportunity to make evidence-based decisions, with strengthened accountability and maximal transparency. The reality of the data revolution, and its embrace by our major governing bodies, means that future generations will need to understand more about data, statistics, and transparent reproducible research than ever before.
This course teaches the basic skills needed to harness the deluge of big data currently underway in virtually all scientific disciplines. The course consists of three components that align with the skills requirements of a modern data scientist: 1) expertise in statistics, 2) programming and 3) data visualization. These three components are geared towards understanding the possibilities and limitations of data, handling big and small datasets while building transparent and reproducible analyses, and finally communicating data in an unbiased way. Based on this vision, this course will introduce key concepts in statistics, programming and visualization that are commonly used across the natural and social sciences.
Statistics
Students will learn the statistics needed for the design, execution and evaluation of data analysis – all core components of the scientific cycle - and thus essential for performing research. Students will also be able to evaluate the statistical claims that support many actions for sustainability - an essential skill for a change maker. The latter demands not only a comprehensive understanding of the key concepts and assumptions but also experience in performing such analyses in order to critically evaluate the merits of the chosen analysis.
Programming
Today, scientists from the health, biological and social sciences have become increasingly concerned with the reproducibility of research. Crucial in this framework are reproducible science workflows that enable transparent data analyses. To be prepared for the future, students should be able to evaluate and implement these workflows.
Visualization
Finally, the old idiom of “a picture is worth a thousand words” rings as true as ever. The course teaches the basics of effective communication of data by teaching the basics skills of visualization for the 21st century.
Content
We will use real-world examples to highlight the greatest triumphs and failures of data science. We will integrate these real-world examples with theory and applied exercises that build in complexity starting from core concepts as the central limit theorem and progressing towards linear regression (ANOVAs, ANCOVAs), multiple regression and multivariate analysis. Special attention is paid to meta-analysis, as this method – in addition to the statistical methods identified above – is often used in studies on socio-ecological systems.
Course objectives
After completing this course, students will be able to:
1. Explain the fundamental statistical concepts, including its assumptions, needed for the execution of statistical analysis and for evaluating statistical claims
2. Select the best suitable statistical design for a given research question and study design
3. Run and interpret statistical analysis for a selected suite of statistical methods
4. Critically verify and judge statistical claims in literature and recognize and address common statistical pitfalls.
5. Use a modern statistical programming language and implement reproducible workflows.
6. Understand the basics of effective data visualization
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
We will have interactive sessions followed by computer labs where students may work in pairs. Within each of the interactive sessions, we will explain new concepts and tools, deal with questions and comments on pre-recorded videos on the course "video wall". We ask for active participation of the students, which may include bringing in their own examples suitable to the topic discussed that session. Moreover, we will discuss the homework from the previous session as well as introduce new homework.
Homework consists of hands-on assignments done in Rmarkdown that students can work on in computer labs under supervision. Assignments consist of executing their own analysis and provide the appropriate interpretation, or analyzing claims from (short) scientific papers – in addition to watching and digesting the information provided. All analyses are assumed to be done in RStudio for which essential codes for the assignments are provided in the assignment texts.
To successfully pass, students must achieve an average grade of at least 5.5 across all assignments and the participation component. It's important to note that there is an opportunity for students to resit any or all the graded assignments in the semester following the course.### Assessment method
Assessment is based on three individual assignments completed in Rmarkdown, each contributing to 30% of the final grade. These assignments involve executing statistical analyses and interpretations on real-world questions with distinct datasets or evaluating claims from the literature.
Each assignment is evaluated using a specific rubric, and the accumulated scores determine the grades. Lecturers inspect the graded assignments and provide feedback to each student privately via email. Additionally, the assignments are discussed collectively during lectures, offering a broader understanding.
Beyond the assignments, there's a 10% grade allocated for participation. This participation grade is determined by students completing online quizzes prior to the commencement of assignment weeks. There are three assessment points throughout the semester for this participation component.
To successfully pass, students must achieve an average grade of at least 5.5 across all assignments and the participation component. It's important to note that there is an opportunity for students to resit any or all of the graded assignments in the semester following the course.
Course Load
The course load is calculated to include 60 hours of lectures, and 79 hours computer labs and guided on-campus study. Students are expected to complete 29 hours of self-study which include reading suggested articles and watching the course “video wall”. A weekly breakdown of the course load and planning is available on Brightspace.
Reading list
BrightSpace University Leiden will be used for communications and distributing study material. A teams channel will be available for homework help.
Registration
Every student of all years must enroll via MyStudyMap.
In this short video, you will see step by step how to enroll in courses in MyStudyMap. Note that your enrollment is only completed when you submit your course planning in the 'Ready for enrollment' tab by clicking 'submit'.
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 register for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam. Keep in mind that there are enrollment deadlines, see this page for more information.
Extensive FAQ on MyStudymap can be found here.
Contact
Prof.dr. Peter van Bodegom (p.m.van.bodegom@cml.leidenuniv.nl)
Dr. ir. Marco Visser (m.d.visser@cml.leidenuniv.nl)
Remarks
MSc Governance of Sustainabilty students can register for the course and exam via uSis. Other students need to contact the study advisors of the programme via studyadvisor-gofs@cml.leidenuniv.nl