VOCATIONAL SCHOOL

Department of Computer Programming (Turkish)

MBP 200 | Course Introduction and Application Information

Course Name
Visual Programming III
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MBP 200
Fall/Spring
2
2
3
4

Prerequisites
None
Course Language
Turkish
Course Type
Elective
Course Level
Short Cycle
Mode of Delivery face to face
Teaching Methods and Techniques of the Course Group Work
Problem Solving
Application: Experiment / Laboratory / Workshop
Lecture / Presentation
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives Students who successfully complete this course will be able to comprehend object oriented programming examples, design a visual interface and perform database applications.
Learning Outcomes The students who succeeded in this course;
  • Will be able to do database applications
  • Will be able to define the algorithm term
  • Will be able to design using forms and interfaces
  • Will eb able to apply object oriented programming
  • Will be able to create graphical interfaces
Course Description Algorithm concept, object oriented programming basics, designing visual interface, performing sample applications with buttons and controls, designing and drawing graphical interface, introduction to database, debugging

 



Course Category

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 Fundamentals of Visual Programming and Database Samancıoğlu, A., “PYTHON – SIFIRDAN UZMANLIĞA PROGRAMLAMA”, UNİKOD YAYIN DAĞITIM (2021). Part 41
2 Visual Programming and Database Applications Samancıoğlu, A., “PYTHON – SIFIRDAN UZMANLIĞA PROGRAMLAMA”, UNİKOD YAYIN DAĞITIM (2021). Part 41
3 File Operations Samancıoğlu, A., “PYTHON – SIFIRDAN UZMANLIĞA PROGRAMLAMA”, UNİKOD YAYIN DAĞITIM (2021). Part 35
4 Numpy Library and Applications- 1 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 2
5 Numpy Library and Applications- 2 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 2
6 Pandas Library and Applications- 1 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 3
7 Pandas Library and Applications- 2 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 3
8 Mid-term
9 Pandas Library and Applications- 3 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 3
10 Matplotlib Library and Applications Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 4
11 Machine Learning- Unsupervised Learning Applications- 1 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 6
12 Machine Learning- Unsupervised Learning Applications- 2 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 7
13 Machine Learning- Unsupervised Learning Applications- 3 Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 8
14 Machine Learning- Supervised Learning Applications Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021). Part 10-11
15 Review of the semester
16 Final

 

Course Notes/Textbooks

Samancıoğlu, A., “PYTHON – SIFIRDAN UZMANLIĞA PROGRAMLAMA”, UNİKOD YAYIN DAĞITIM (2021) (ISBN: 9786257066075)
Uğuz, S., “MAKİNE ÖĞRENMESİ- TEORİK YÖNLERİ VE PYTHON UYGULAMALARI İLE BİR YAPAY ZEKA EKOLÜ”, NOBEL AKADEMİK YAYINCILIK (2021) (ISBN: 9786050331769)

Suggested Readings/Materials

Geron, A., "HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN & TENSORFLOW ", O’Reilly Media (2017) (ISBN: 9781491962299)

 

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
1
40
Total

Weighting of Semester Activities on the Final Grade
1
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
16
2
32
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
1
10
10
Seminar / Workshop
0
Oral Exam
0
Midterms
1
10
10
Final Exam
1
10
10
    Total
126

 

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.

X
5

To be able to use basic programming languages related to the field.

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.

X
9

To have basic theoretical and practical knowledge about mathematics, computing and computer science.

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

 


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