Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05041080 | Applıcatıon Development Wıth Python | 2 | Spring | 4 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Ali Osman ÇIBIKDİKEN | A-124 | [email protected] | 7585 | Monday 14.00-15.00 |
Course Content
Basic programming steps of python, structural elements of python, libraries of python, programming tools that helps specific computer science needs like machine learning are constitute the python class.
Objectives of the Course
This introduction to Python will give students basics of programming on python language. Students learn syntax, libraries, IDEs of python, they become familiar with python systems like TensorFlow
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P3 | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose | 5 |
| P4 | Ability to develop, select and use modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; ability to use information technologies effectively | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Learning at least one object-oriented programming language. | P.3.3 | 1,7 |
| O2 | Knowledge and use of software development platforms. | P.3.5 | 1,7 |
| O3 | Algorithm development knowledge and creation of appropriate data structure for the algorithm. | P.4.7 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction |
| 2 | gitHub, Functions, Booleans and Modules |
| 3 | Sequences, Iteration and String Formatting |
| 4 | Dictionaries, Sets, and Files |
| 5 | Exceptions, Testing, Comprehensions |
| 6 | Advanced Argument Passing, Lambda functions as objects |
| 7 | TensorFlow |
| 8 | Advanced Argument Passing, Lambda functions as objects |
| 9 | Decorators, Context Managers, Regular Expressions, and Wrap Up |
| 10 | Database Applications I |
| 11 | Database Applications II |
| 12 | Creating Modern GUI Applications I |
| 13 | Creating Modern GUI Applications II |
| 14 | Final |
Textbook or Material
| Resources | Learning Python, Fabrizio Romano,2015, Packt |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 1 | 14 |
| Midterms | 1 | 3 | 3 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 2 | 2 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 61 | ||
| Total Work Load / 30 | 2,03 | ||
| Course ECTS Credits: | 2 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P3 | P4 |
|---|---|---|---|
| O1 | Learning at least one object-oriented programming language. | 4 | 5 |
| O2 | Knowledge and use of software development platforms. | 2 | - |
| O3 | Algorithm development knowledge and creation of appropriate data structure for the algorithm. | - | 4 |