VOCATIONAL SCHOOL
Department of Computer Programming (Turkish)
MBP 227 | Course Introduction and Application Information
| Course Name |
Data Science and Machine Learning
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MBP 227
|
Fall/Spring
|
2
|
2
|
3
|
4
|
| Prerequisites |
None
|
|||||
| Course Language |
Turkish
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Short Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Group WorkProblem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The aim of this course is to introduce students to the techniques used to gather information, model data and extract information from large amounts of data. Within the scope of the course, students will be able to make prediction applications on data sets with machine learning algorithms. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course includes data analysis, data visualization methods, data manipulation, feature engineering, supervised and unsupervised machine learning techniques. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Data Analysis- 1 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 2 |
| 2 | Data Analysis- 2 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 2 |
| 3 | Data Analysis- 3 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 3 |
| 4 | Data Visualization and Interpretation- 1 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 4 |
| 5 | Data Visualization and Interpretation- 2 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 4 |
| 6 | Outlier Analysis | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 5 |
| 7 | Missing Value Analysis | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 5 |
| 8 | Midterm Exam | |
| 9 | Data Transformation and Feature Extraction | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 6 |
| 10 | Basic Concepts in Machine Learning | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 5 |
| 11 | Machine Learning- Unsupervised Learning Applications- 1 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 7 |
| 12 | Machine Learning- Unsupervised Learning Applications- 2 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 8 |
| 13 | Machine Learning- Supervised Learning Applications- 1 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 10 and 11 |
| 14 | Machine Learning- Supervised Learning Applications- 2 | Uğuz, S., “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). Chapter 12 |
| 15 | Review of the Semester | |
| 16 | Final Exam |
| Course Notes/Textbooks | Sinan Uğuz, “Makine Öğrenmesi - Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü”, Nobel Akademik Yayıncılık (2021). (ISBN: 9786050331769) |
| Suggested Readings/Materials |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
5
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
10
|
| Presentation / Jury | ||
| Project |
1
|
20
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
25
|
| Final Exam |
40
|
|
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
| Study Hours Out of Class |
14
|
1
|
14
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
5
|
5
|
| Presentation / Jury |
0
|
||
| Project |
1
|
9
|
9
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
11
|
11
|
| Final Exam |
17
|
0
|
|
| Total |
103
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to have basic computer hardware and software knowledge. |
X | ||||
| 2 | To be able to develop the necessary applications by using internet and network technologies. |
|||||
| 3 | To follow developments in the field to adapt to changing conditions. |
|||||
| 4 | To be able to conduct experiments in the field and analyze the results. |
|||||
| 5 | To be able to use basic programming languages related to the field. |
X | ||||
| 6 | To be able to design and install a computer system that includes software, hardware, or both, meeting the basic needs of the field. |
X | ||||
| 7 | To be able to interpret and follow current developments in the field of computer programming. |
X | ||||
| 8 | To be able to carry professional and ethical responsibility and have awareness of professional ethics in their practices. |
|||||
| 9 | To have basic theoretical and practical knowledge about mathematics, computing and computer science. |
X | ||||
| 10 | To be able to follow the information in the field and communicate with colleagues by using English at the general level of European Language Portfolio A2. |
|||||
| 11 | To be able to direct his/her education to a further level of education |
|||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
NEWS |ALL NEWS
'Success' formula of the masters
Former CEO of Turkcell and investor Süreyya Ciliv, who held senior positions in high technology companies, met with students at the "Students
'Digital' mobilization
Izmir University of Economics (IUE) started a 'digital transformation' movement in order for students to improve themselves in this field by not