Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA SCIENCE APPLICATIONS 55497013 2 + 2 3.0 6.0

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.
4) Benford group various data using analysis.
5) Classifies according to standard deviation by statistical analysis on existing data.
6) Transfer the data to the graph and comment.

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

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 LevelDK1DK2DK3DK4DK5DK6
PY15555555
PY25455555
PY35000000
PY45000000

*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 4
Presentation (Including Preparation Time) 1 15
Project (Including Preparation and presentation Time) 1 15
Report (Including Preparation and presentation Time) 1 10
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 15
Final Exam 1 2
Time to prepare for Final Exam 1 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload