Computer Programming
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 03841202 | Current technologies in informatics | 2 | Spring | 4 | 2+1+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | 1. Teorik Anlatım: Konular ders kapsamında teorik olarak anlatılır. Öğrenciler programlamanın temel kavramlarını ve algoritmaların mantığını kavrayabilmeleri için konu anlatımları dinler. 2. Uygulamalı Çalışmalar: Öğrenciler, teorik olarak anlatılan konuların uygulamasını yapmak için ders eğitmeni mentorlüğünde çeşitli örneklerle çalışmalar gerçekleştirir. Kazanımlar elde edilmeye çalışılır. 3. Adım Adım Çözümleme: Karşılaşılan problemler adım adım çözülerek her adımın nasıl işlediği açıklanır. Bu yöntemle öğrencilerin konulara daha hakim olması sağlanır. 4.Gerçek Hayat Örnekleri: Konuların daha iyi anlaşılması için gerçek hayattan örnekler ve problem senaryoları sunulur. Böylece öğrenciler öğrendiklerini pratikte nasıl kullanacağını görür. 5. Laboratuvar Föyleri ve Quizler: Haftalık laboratuvar föyleri ve sınav öncesi quizler ile öğrencilerin ilerlemesi değerlendirilir, konuların anlaşılıp anlaşılmadığı takip edilir. |
| Mode of Delivery | Face to Face |
| Prerequisites | Dersin herhangi bir ön koşulu bulunmamaktadır. Tüm öğrencilere temel seviyeden başlanarak eğitim verilmektedir. |
| Coordinator | - |
| Instructor(s) | Lect. Uğur POLAT |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Uğur POLAT | TSMYO-T213 | [email protected] | 7860 | Monday 15.00-16.00 |
Course Content
1. Introduction and Digital Transformation
2. Project Management
3. Artificial Intelligence and Machine Learning
4. Big Data and Data Analytics
5. Cloud Computing and Service Models
6. Internet of Things (IoT)
7. Blockchain and Cryptocurrencies
8. Cyber Security and Threats
9. Augmented Reality (AR) and Virtual Reality (VR)
10. 5G and Advanced Connectivity Technologies
11. Autonomous Systems and Robotics
12. Intersection of Biotechnology and Informatics
13. Sustainable Technologies and Green Informatics
14. Future Technologies and Trends
2. Project Management
3. Artificial Intelligence and Machine Learning
4. Big Data and Data Analytics
5. Cloud Computing and Service Models
6. Internet of Things (IoT)
7. Blockchain and Cryptocurrencies
8. Cyber Security and Threats
9. Augmented Reality (AR) and Virtual Reality (VR)
10. 5G and Advanced Connectivity Technologies
11. Autonomous Systems and Robotics
12. Intersection of Biotechnology and Informatics
13. Sustainable Technologies and Green Informatics
14. Future Technologies and Trends
Objectives of the Course
The Current Technologies in Informatics course aims to introduce students to the latest innovations and developments in the field of informatics and to help them understand the rapidly changing trends in the world of technology. Within the scope of this course, students will explore current technologies such as cloud computing, artificial intelligence, big data, the internet of things (IoT), blockchain, cybersecurity and 5G, and analyze the potential impacts of these technologies on the business world, social structure and the future. At the end of the course, students will have mastered the latest developments in informatics, understand the basic principles of these technologies and have the necessary knowledge infrastructure to develop innovative projects.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | X |
| 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 |
|---|---|---|
| P4 | Effectively uses information technologies (software, programs, animations, etc.) related to her/his profession. | 5 |
| P1 | He/she has basic, current and applied information about his/her profession. | 5 |
| P3 | He/She follows current developments and practices in his profession and uses them effectively. | 5 |
| P5 | Has the ability to independently evaluate professional problems and issues with an analytical and critical approach and propose solutions. | 4 |
| P6 | Can present his/her thoughts effectively through written and verbal communication at the level of knowledge and skills and expresses them in an understandable manner. | 3 |
| P11 | Creates algorithms and data structures and performs mathematical calculations. | 4 |
| P14 | Tests software and fixes bugs. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows how to develop algorithms and creates a data structure suitable for the algorithm. | P.4.1 | 1,7 |
| O2 | Ability to use artificial intelligence methods | P.4.4 | 1,7 |
| O3 | Have knowledge about current programming languages. | P.4.5 | 1,7 |
| O4 | Knows the basic elements of a computer. | P.1.1 | 1,7 |
| O5 | Knows how to use the internet and do research. | P.1.2 | 1,7 |
| O6 | Can perform basic mathematical analyses related to his/her profession. | P.1.3 | 1,7 |
| O7 | Knows current techniques for data analysis. | P.3.1 | 1,7 |
| O8 | Must know and use current software development platforms. | P.3.2 | 1,7 |
| O9 | Have basic analysis knowledge. | P.3.5 | 1,7 |
| O10 | Tests software and fixes bugs. | P.5.1 | 1,7 |
| O11 | Evaluate computer science topics and algorithms using critical thinking skills | P.5.4 | 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 and Digital Transformation |
| 2 | Project Management |
| 3 | Artificial Intelligence and Machine Learning |
| 4 | Big Data and Data Analytics |
| 5 | Cloud Computing and Service Models |
| 6 | Internet of Things (IoT) |
| 7 | Pre-Exam Quiz, Blockchain and Cryptocurrencies |
| 8 | Midterm exam |
| 9 | Cyber Security and Threats |
| 10 | Augmented Reality (AR) and Virtual Reality (VR) |
| 11 | 5G and Advanced Connectivity Technologies |
| 12 | Autonomous Systems and Robotics |
| 13 | Intersection of Biotechnology and Informatics |
| 14 | Sustainable Technologies and Green Informatics |
| 15 | Pre-Exam Quiz, Future Technologies and Trends |
| 16 | Final Exam |
Textbook or Material
| Resources | Stuart Russell ve Peter Norvig, "Artificial Intelligence: A Modern Approach" |
| Thomas Erl, Ricardo Puttini, Zaigham Mahmood, "Cloud Computing: Concepts, Technology & Architecture" |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | 1 | 20 (%) |
| Projects | - | - |
| Seminar | - | - |
| Quiz | 2 | 10 (%) |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 40 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 16 | 4 | 64 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 8 | 8 |
| Quiz | 2 | 4 | 8 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 1 | 6 | 6 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 18 | 18 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 146 | ||
| Total Work Load / 30 | 4,87 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P3 | P4 | P5 |
|---|---|---|---|---|---|
| O1 | Knows the basic elements of a computer. | 4 | - | - | - |
| O2 | Knows how to use the internet and do research. | 4 | - | - | - |
| O3 | Can perform basic mathematical analyses related to his/her profession. | 4 | - | - | - |
| O4 | Knows current techniques for data analysis. | - | 4 | - | - |
| O5 | Must know and use current software development platforms. | - | 5 | - | - |
| O6 | Have basic analysis knowledge. | - | 5 | - | - |
| O7 | Knows how to develop algorithms and creates a data structure suitable for the algorithm. | - | - | 5 | - |
| O8 | Ability to use artificial intelligence methods | - | - | 5 | - |
| O9 | Have knowledge about current programming languages. | - | - | 5 | - |
| O10 | Tests software and fixes bugs. | - | - | - | 5 |
| O11 | Evaluate computer science topics and algorithms using critical thinking skills | - | - | - | 5 |