Duration
30h Th
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course provides a hands-on introduction to Data Wrangling with the python programming language. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.
The focus of the course is mainly applied and aims at directly putting the tools to practice.
Learning outcomes of the learning unit
Upon successfully completing this course, you will be able to:
- Perform your data analysis in a literate programming environment
- Manage different types of data
- Import, scrape, and export data
- Compute descriptive statistics
- Visualize data
Prerequisite knowledge and skills
No prior experience is required with any of the software used in class. But you should have already used a statistical or programming software at an introductory level.
Most of all, you should have a taste for coding, collaborating, and looking for answers on the internet.
Planned learning activities and teaching methods
The course mixes interactive lectures and tutorial sessions. Therefore, students must attend the class in person and no broadcast will be possible.
You should plan on bringing your own laptop to class.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face-to-face lectures and tutorials.
Students are asked to proactively participate during the lectures and tutorials.
Course materials and recommended or required readings
All required classroom material will be provided in class or online. Any recommended yet optional material will also be provided in the classroom notes.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Continuous assessment
Further information:
Additional information:
End-term project: written report in the form of a python notebook + oral presentation.
4 problem sets to solve during the term.
Class participation is also important.
Work placement(s)
Organisational remarks and main changes to the course
Website of the class: https://malkaguillot.github.io/ECON2206-Data-Management
Contacts
Professor: Malka Guillot, mguillot@uliege.be
Assistant: Nicolas Marissiaux, nicolas.marissiaux@uliege.be