Admission requirements
There are no admission requirements.
Description
Data are all around us; and data are playing an increasingly important role in modern-day life and science. Therefore, gaining insights from large quantities of data has also become increasingly important. In this course, students will learn the principles of data science and essential programming skills in the R programming language. Students will learn how to find, import, and preprocess raw data. Students will learn how to organize and manage data. And, students will learn how these data can then be used for data visualizations, analytics, and basic models; that is, for exploratory data analysis.
The course relies on hands-on programming and provides an introduction to programming in R using R Studio. Students learn how to use libraries and how to write scripts that generate reproducible outputs. Students learn how to deal with errors. Students learn to construct and manipulate data structures, including lists, vectors, and data frames. Students learn to write expressions, conditions, loops, and functions. Students learn to create various types of plots that fit the data. Finally, students learn the basics of linear models as well as the limitations with respect to inference.
Course objectives
At the end of this course, students will be able to:
Describe the principles of data science.
Recognize dataset structures.
Find, import, transform, and clean data.
Prepare data for visualization.
Generate descriptives and analytics from datasets and variables.
Apply linear models and understand the limitations with respect to inference.
Phrase follow-up research questions.
Timetable
Zie MyTimetable.
Mode of instruction
Lectures
Number of (2 hour) lectures: 1 introductory lecture of 2 hours (mandatory)
Names of lecturers: Dr. J.W.A.M. Steegmans
Required preparation by students:
Seminars
Number of (2 hour) seminars: 7 tutorials of 2 hours each (mandatory)
Names of lecturers: Dr. J.W.A.M. Steegmans
Required preparation by students:
Assessment method
Examination form(s)
Two submission assignments (20% each; 40% in total)
Final assignment (60%)
The smaller submission assignments may be compensated by the final assignment; the final assignment has to be completed with a score of 5.5 or higher. To complete the course, the final weighted grade must be a score of 5.5 or higher. A retake will only be available for the final assignment. Grades cannot be carried over to the next year in case a student repeats the course.
Reading list
Obligatory course materials
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, Inc. Online version available at: https://r4ds.hadley.nz/
Links to further reading materials can be found on Brightspace.
Registration
Registration for courses and exams takes place via MyStudymap. If you do not have access to MyStudymap (guest students), look here (under the Law-tab) for more information on the registration procedure in your situation.
Contact
Coordinator: Dr. J.W.A.M. Steegmans
Work address: Wijhaven, room 3.26
Email: Steegmans, J.W.A.M. (Joep) <j.w.a.m.steegmans@law.leidenuniv.nl>
Institution/division
Institute: Tax Law and Economics
Department: Economics
Room number secretary: Kamerlingh Onnes Building, room B2.07
Opening hours: Monday to Friday 9.00 – 12.00
Telephone number secretary: +31 (0)71 527 7756
Email: LAW - Economie <economie@law.leidenuniv.nl>