Admission requirements
This course is only available for Bachelor Bestuurskunde students. A minimum level of familiarity with Python and statistics is required for this class. Students who wish to enroll in this course, but who have not passed part I (Data Science Skills and Applications I: Basic Programming with Python - 40322CDS1), should contact the lecturer in advance.
Description
Policy makers and researchers face the complex challenge of designing governance strategies and understanding the social implications of decisions using data analytics. This course provides students with the tools to employ common analytical approaches within computational social sciences, emphasizing their inherent advantages, limitations, and possible uses in the context of policy making.
Using Python as our primary tool, students will gain the essential skills necessary to explore, analyze, present, and critically evaluate data analysis results. From data collection to analysis and interpretation, students will receive knowledge on how implementing controls and recognizing constrains of prevalent computational methods. Through interactive exercises, students will gain hands-on experience in leveraging data analytics to drive evidence-based policy decisions.
This course offers to future policy makers, researchers, or those interested in the intersection of data analytics and social sciences a dynamic platform to expand their knowledge and skills in leveraging data.
Course objectives
Develop the ability in organizing simple datasets.
Acquire the knowledge to select and apply appropriate computational methods for analyzing data.
Obtain hands-on experience with most commonly used Python libraries for data analysis.
Interpret and critically evaluate results of different analytical methods.
Timetable
On the right side of programme front page of the E-guide you will find links to the website and timetables, MyStudymap, uSis and Brightspace.
Mode of instruction
The course consists of seven (7) lectures, each lasting two (2) hours, and six (6) practical sessions, also two (2) hours each.
Assessment method
Three (3) individual assignments (60 % or the course grade).
One (1) project:
- Presentation (15% of the course grade)
- 2000 words paper (20% of the course grade)
- Average grade from peer review of the presentation by classmates (5% of the course grade).
- Presentation (15% of the course grade)
The project is completed in groups of three (3) students.
The weighted grade for the group project should be 5.5 or higher in order to complete the course.
The weighted average grade for the practical assignments should be 5.5 or higher in order to complete the course.
A submission after the deadline is counted as re-sit. When submitted as re-sit, the maximum grade for an assignment is 7.
If one of the tasks is not submitted the grade for that task is 0.
Partial grades are only valid in the current academic year; partial grades will not remain valid after the exam and the resit of the course.
Reading list
There is no textbook or reading list for this course. literature and reading materials will be announced during the course.
Registration
Register yourself via MyStudymap for each course, workgroup and exam (not all courses have workgroups and/or exams). Do so on time, before the start of the course; some courses and workgroups have limited spaces. You can view your personal schedule in MyTimetable after logging in.
Registration for this course is possible from Tuesday 9 December, 13:00 h.
Please note: guest-/contract-/exchange students do not register via MyStudymap but via uSis. Registration via uSis is possible from Thursday 11 December 2025.
Leiden University uses Brightspace as its online learning management system. After enrolment for the course in MyStudymap you will be automatically enrolled in the Brightspace environment of this course.
More information on registration via MyStudymap can be found on this page.