Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ARCHAEOLOGICAL DATA ANALYSIS IN FORENSIC SCIENCES ANT5702011 3 + 0 3.0 6.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The aim of this course is to provide the theoretical and practical background for the classification of the data collected and studied in the field of Forensic Sciences and to obtain the desired results by using the appropriate statistical methods.
Course Content Classification and representation of data; basic statistical methods; hypothesis testing using different methods (regression, correlation, principle component and discriminant function analysis); cluster analysis.
Learning Outcomes 1) Gains the ability to define and accurately classify the data studied in the field of Forensic Sciences.
2) Gains knowledge about basic statistical methods and their applications.
3) Gains the ability to determine and apply the appropriate statistical method based on the available data and the desired results.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Working with the data: Description and Classification Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
2. Week Basic Statistical Concepts 1 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
3. Week Basic Statistical Concepts 2 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
4. Week Representation of data, graphics Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
5. Week Corelation Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
6. Week Regression Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
7. Week Principle Component Analysis Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
8. Week Discriminent Function Analysis Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
9. Week Cluster Analysis Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
10. Week Hypothese testing Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
11. Week Case studies 1 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
12. Week Case studies 2 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
13. Week Student presentations 1 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)
14. Week Student presentations 2 Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
"M. Baxter, 2016. Multivariate Analysis of Archaeometric Data, An Introduction. Nottingham. "
"T. L. Van Pool - R. D. Leonard, 2011. Quantitative Analysis in Archaeology. Wiley-Blackwell, West Sussex. ""T. L. Van Pool - R. D. Leonard, 2011. Quantitative Analysis in Archaeology. Wiley-Blackwell, West Sussex. "
S. Shennan, 1997. Quantifying Archaeology (Second Edition). Edinburgh University Press, Edinburgh.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY45555
PY65444
PY75444
PY85555
PY135444
PY205555
PY215555
PY225444

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 2
Work Hour outside Classroom (Preparation, strengthening) 14 4
Homework 6 3
Presentation (Including Preparation Time) 2 11
Project (Including Preparation and presentation Time) 1 10
Report (Including Preparation and presentation Time) 1 10
Final Exam 1 2
Time to prepare for Final Exam 1 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload