DATS5289 Thesis 1Istinye UniversityDegree Programs Data Science (Master) (with Thesis) (English)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Data Science (Master) (with Thesis) (English)

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Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code: DATS5289
Course Name: Thesis 1
Semester: Fall
Course Credits:
ECTS
30
Language of instruction: English
Course Condition:
Does the Course Require Work Experience?: No
Type of course: Compulsory Courses
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: E-Learning
Course Coordinator: Araş. Gör. KAZIM TİMUÇİN UTKAN
Course Lecturer(s): Doç. Dr. ŞEBNEM ÖZDEMİR
Course Assistants:

Course Objective and Content

Course Objectives: It aims to develop students' academic research skills, to carry out an independent and original research, to manage this process within the framework of ethical rules and to increase their academic communication skills by producing a scientific written work. The main objective of this course is to enable the student to identify a problem specific to his/her chosen field, to gain in-depth knowledge about this problem, to select and apply appropriate research methods and to produce original results with his/her own analyses.
Course Content: 1. Examination and application of research methodologies.
2. Literature review techniques and information management.
3. Scientific data analysis, modelling and interpretation.
4. Academic writing techniques and ethical rules.
5. Thesis writing process and presentation techniques.

Learning Outcomes

The students who have succeeded in this course;
1) 1. The student will have the ability to conduct an independent research.
2) 2. will be able to select and apply the necessary methods for scientific research.
3) 3. will be able to use academic writing and presentation techniques effectively.
4) 4. will be able to act in accordance with research ethics and copyrights.
5) 5. will be able to analyze the findings and turn them into a scientific written report.

Course Flow Plan

Week Subject Related Preparation
1) Final -
2) Thesis study -
3) Thesis study -
4) Thesis study -
5) Thesis study -
6) Thesis study -
7) Thesis study -
8) Thesis study -
9) Thesis study -
10) Thesis study -
11) Thesis study -
12) Thesis study -
13) Thesis study -
14) Thesis study -
15) Thesis study -
16) Final -

Sources

Course Notes / Textbooks: Herhangi bir ders kitabı bulunmamaktadır.
There is no textbook.
References: Güncel makaleler, kitaplar kullanılacaktır.
Current articles and books will be used.

Course - Program Learning Outcome Relationship

Course Learning Outcomes

1

2

3

4

5

Program Outcomes
1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. 3 3 3 3 3
2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. 3 3 3 3 3
3) Students who complete this program; Recognize the mathematics and code in application processes 3 3 3 3 3
4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual 3 3 3 3 3
5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. 3 3 3 3 3

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Average 3 Highest
       
Program Outcomes Level of Contribution
1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. 3
2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. 3
3) Students who complete this program; Recognize the mathematics and code in application processes 3
4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual 3
5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. 3

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Presentation 1 % 100
total % 100
PERCENTAGE OF SEMESTER WORK % 100
PERCENTAGE OF FINAL WORK %
total % 100

Workload and ECTS Credit Calculation

Activities Number of Activities Workload
Course Hours 16 32
Presentations / Seminar 16 711
Total Workload 743