| Data Science (Master) (with Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | VB5289 | ||||
| Course Name: | Master Thesis | ||||
| Semester: | Fall | ||||
| Course Credits: |
|
||||
| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Compulsory Courses | ||||
| Course Level: |
|
||||
| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): |
Doç. Dr. OKAN YAŞAR Doç. Dr. ŞEBNEM ÖZDEMİR |
||||
| Course Assistants: |
| Course Objectives: | It aims to develop students' academic research skills, to conduct 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 students to identify a problem specific to their chosen field, to gain in-depth knowledge about this problem, to select and apply appropriate research methods and to produce original results with their own analysis. |
| Course Content: | 1. Examination and application of research methodologies. 2. Literature review techniques and information management. 3. Scientific data analysis, modeling and interpretation. 4. Academic writing techniques and ethical rules. 5. Thesis writing process and presentation techniques. |
|
The students who have succeeded in this course;
1) The student will have the ability to conduct an independent research. 2) will be able to select and apply the necessary methods for scientific research. 3) will be able to use academic writing and presentation techniques effectively. 4) will be able to act in accordance with research ethics and copyrights. 5) will be able to analyze the findings and turn them into a scientific written report. |
| Week | Subject | Related Preparation |
| 1) | Selection of the thesis topic and determination of research questions. | |
| 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) | Thesis Study |
| 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 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; Recognizes mathematics and code in application processes. | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of processes in data science on output and 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 |
| 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; Recognizes mathematics and code in application processes. | 3 |
| 4) | Students who complete this program; Explain the effects of processes in data science on output and 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 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Presentation | 1 | % 100 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 100 | |
| PERCENTAGE OF FINAL WORK | % | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Midterms | 8 | 49 |
| Final | 8 | 66 |
| Total Workload | 157 | |