Admission requirements
Admission to the MSc Archaeology programme Archaeological Science.
Quantitative Methods in Archaeology (or a similar) course obtained.
Description
Computational archaeology involves the application of mathematics, statistics, and various other computerised techniques to solve archaeological problems. In this regard, good data analysis and computer scripting practices become necessary. In order to respond to the emerging needs of the discipline and build over the objectives of the "Quantitative Methods in Archaeology" course, the "Archaeological Science Specialisation Course: Computational Archaeology" further introduces essential statistical methods, such as regression analysis, principle component analysis, and correspondence analysis. We use Python to analyse real archaeological data.
Python is a high-level, general-purpose programming language. It is suitable for many applications, ranging from digital humanities to engineering. For instance, most common database structures can be connected to and managed by Python. The language is powerful in GIS applications; users can drastically enhance the capacity of their spatial data projects. Finally, using Python to develop machine learning (ML) and artificial intelligence (AI) solutions to archaeological problems is common. The specialisation course introduces building blocks of programming, such as conditions and loops. Finally, we learn how to write Python functions assisting in archaeological data analysis.
Course objectives
Learning fundamentals of computing;
Gaining coding skills with Python;
Applying statistical methods and workflows over a series of univariate and multivariate datasets
Acquiring a computational perspective for archaeological problems.
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: The instructor introduces theoretical concepts in a classroom setting;
Laboratory work: Students work on archaeological datasets in a lab setting.
Assessment method
Final project – Coding and Reporting (50%);
Assignments (50%).
There is 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.
Reading list
To be announced.
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.
Contact
For more information about this course, please contact Dr. Tuna Kalaycı.
Remarks
Attendance is not compulsory but recommended: preparing the final project and solving assignments relies on class participation. The attendance is also recommended for attending the Introduction to Machine Learning and Artificial Intelligence in Archaeology course. Students are expected to use their laptops.