Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Faculty of Engineering and Natural Science
Programme of Computer Engineering
Course Details
Faculty of Engineering and Natural Science
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05081104 | Advanced Programming | 2025 | Spring | 8 | 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. Neşe ÖZKAN YILMAZ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Neşe ÖZKAN YILMAZ | A BLOK-130 | [email protected] | 7812 |
Course Content
History of Programming Languages, Syntax and Meaning, Control Structures, Data Types, Data Flow, Logic Programming, Functional Programming and Lambda Calculation, Simultaneous and Distributed Programming, Agent-Based Programming, Subject-Based Programming, View-Based Programming, Service-Based Programming.
Objectives of the Course
This course will introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization and text analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, opencv, keras, tensorflow, yolo to gain insight into their data.
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 |
|---|---|---|
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
| 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 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Students gain detailed knowledge of the concepts, costs and limitations of high-level programming languages | P.2.27 | 1 |
| O2 | Students know high-level abstraction techniques of programming. | P.2.28 | 1,7 |
| O3 | Students know programming languages classes. | P.2.29 | 1,7 |
| O4 | Students become familiar with advanced software development principles, techniques and best practices. | P.3.28 | 1,7 |
| O5 | Students have knowledge about programming language fields and their purposes. | P.3.29 | 1 |
| ** 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 and familiarization with the teaching environment. |
| 2 | Introduction to python: the first python program, basics of python syntax, data types of python, basic operations of python, functions, modules and packages of python, conditions, range, loops. |
| 3 | Introduction to python: break, continue and else in loops, self-defined functions, recursions, scope of variable: standart library functions, exceptions. |
| 4 | Data acqusition and presentation |
| 5 | Powerful data structures and python extension libraries: dictionary use, extension library SciPy, ndarray, dataframe |
| 6 | Python data statistics and visualization: data preperations, data display, data selection, simple statistics and processing, grouping, merge, cluster, basics of matplotlib plotting. |
| 7 | Project 1 Presentation |
| 8 | Midterm Exam |
| 9 | Applied machine learning in python |
| 10 | Introduction to keras in python |
| 11 | Digit classification using deep learning and conventional methods |
| 12 | Introduction to Darknet YOLO in python |
| 13 | Object Detection and recognition using YOLO |
| 14 | Project 2 Presentation |
| 15 | Final Exam |
Textbook or Material
| Resources | Robert W. Sebesta: ''Concepts of Programming Languages'', 9th ed., Addison Wesley 2009 |
| Robert W. Sebesta: ''Concepts of Programming Languages'', 9th ed., Addison Wesley 2009 |
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 | 4 | 56 |
| 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 | 3 | 3 |
| Other | 14 | 4 | 56 |
| Total Work Load: | 160 | ||
| Total Work Load / 30 | 5,33 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P3 |
|---|---|---|---|
| O1 | Students gain detailed knowledge of the concepts, costs and limitations of high-level programming languages | 4 | - |
| O2 | Students know high-level abstraction techniques of programming. | 2 | - |
| O3 | Students know programming languages classes. | 5 | - |
| O4 | Students become familiar with advanced software development principles, techniques and best practices. | - | 1 |
| O5 | Students have knowledge about programming language fields and their purposes. | - | 2 |