Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA ANALYSIS METHODS 55499007 2 + 2 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors Recep ERYİĞİT
Assistants
Goals The aim of the course is to examine the methods used in data collection and analysis of collected data in judicial processes.
Course Content Query processing in relational databases, examination of data exchange, analysis of accounting data by Benford method, application of statistical concepts (mean, standard deviation) to various judicial data.
Learning Outcomes 1) Know the structure of relational databases.
2) Performs query operations on relational databases.
3) It associates the query results with the operating system.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Basic concepts of relational databases Lecture; Question Answer; Discussion
Opinion Pool; Colloquium
Problem Based Learning 		Homework Presentation (Including Preparation Time)
2. Week Query in relational databases Lecture
Brainstorming
Project Based Learning 		Homework
3. Week Query in relational databases Lecture
Brainstorming
Project Based Learning 		Homework
4. Week Query in relational databases Lecture
Brainstorming
Project Based Learning 		Homework
5. Week Examination of log records in relational databases Lecture
Brainstorming
Project Based Learning 		Homework
6. Week Benford Data analysis method Lecture
Brainstorming
Project Based Learning 		Homework
7. Week Functions, formulas and equations Lecture
Brainstorming
Project Based Learning 		Homework
8. Week Exponential and logarithmic functions and applications Lecture
Brainstorming
Project Based Learning 		Homework
9. Week Trigonometric methods in forensic sciences Lecture
Brainstorming
Project Based Learning 		Homework
10. Week Graphics - creation and interpretation Lecture
Brainstorming
Project Based Learning 		Homework
11. Week Statistical analysis of data Lecture
Brainstorming
Project Based Learning 		Homework
12. Week Probability in forensic science Lecture
Brainstorming
Project Based Learning 		Homework
13. Week Statistical evaluation of experimental data: comparison and confidence. Lecture
Brainstorming
Project Based Learning 		Homework
14. Week The importance of statistics and evidence Lecture
Brainstorming
Project Based Learning 		Homework

Sources Used in This Course
Recommended Sources
Essential Mathematics and Statistics for Forensic Science, Craig Adam,Wiley-Blackwell, 2010
Learning SQL: Master SQL Fundamentals, Alan Beaulieu, O'Reilly, 2009

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15555
PY25000
PY35000
PY45000

*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 4
Work Hour outside Classroom (Preparation, strengthening) 14 2
Presentation (Including Preparation Time) 1 10
Project (Including Preparation and presentation Time) 1 10
Report (Including Preparation and presentation Time) 1 5
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 15
Final Exam 1 2
Time to prepare for Final Exam 1 20
1 2
1 2
1 1
1 1
1 2
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