Admission requirements
None.
Description
In the course, we learn how to use introductory-level statistical tools and methodologies in the solution of archaeological questions. The course also offers an introduction to Python scripting.
Lectures focus on theory. We discuss key topics, such as data visualisation, confidence intervals, and hypothesis testing. To practice theory, we use basic coding skills. In lab sessions, we get familiar with Python principles.
In addition, the lab material includes the use of useful packages, such as Numpy and Seaborn.
We aim for open-science practices throughout the course.
Course set-up
Presentation of course material in a lecture setting;
Archaeological data analysis in lab sessions.
Course objectives
Acquiring basic statistical skills;
Building critical thinking about the use of quantitative methods in archaeological research;
Gaining abilities to read published reports, articles, and manuscripts;
Developing basic Python coding skills and performing archaeological computation;
Practicing collaborative learning and exploring the benefits of being intellectually dependable.
Timetable
Course schedule details can be found in MyTimetable.
Log in with your ULCN account, and add this course using the 'Add timetable' button.
Mode of instruction
Lectures;
Laboratory work.
Assessment method
Final project – Coding and Reporting (50%)
Assignments (50%)
There will be one final grade. Passing the average grade is sufficient.
Assessment deadlines:
The dates of exams and retakes can be found in MyTimetable. The deadlines of papers, essays and assignments are communicated through Brightspace.
Lab assignments are due throughout the block. Peer reviews follow. The final project is due in the reading/8th week.
Reading list
Not applicable.
Registration
Enrolment for all components of your study programme through MyStudymap is mandatory. This applies to both compulsory elements and elective credits. If you are not enrolled, you may not participate.
General information about registration can be found on the Course and exam enrolment page.
Exchange and Study Abroad students, please contact the exchange coordinator for information on how to apply.
Contact
For more information about this course, please contact Dr. T. (Tuna) Kalayci.
Remarks
Attendance is not compulsory but recommended. Labs are essential for gaining necessary quantitative skills. Attendance is especially advised if you want to enrol in the elective course Introduction to Machine Learning and Artificial Intelligence in Archaeology.