COURSE DESCRIPTION AND APPLICATION INFORMATION
| Course Name | Code | Semester | T+A+L (hour/week) | Type (C / O) | Local Credit | ECTS |
|---|---|---|---|---|---|---|
| Computational Thinking | KHAS 109 | Fall | 03+00+00 | Compulsory | 3 | 5 |
| Academic Unit: | Core Program |
| Mode of Delivery: | Face to face |
| Prerequisites: | - |
| Language of Instruction: | English |
| Level of Course Unit: | Undergraduate |
| Course Coordinator: | - - |
| Course Objectives: | This course aims to present an applied introduction to algorithmic thinking for complex problem solving tasks. It seeks to build up a wide variety of interdisciplinary problem and conflict-resolution skills and competencies derived from computation, mathematics, logic and design. It introduces a multitude of problem solving skills such as pattern recognition, abstraction, induction-deduction that students will work on in groups, as well as preparing students to use programming interfaces like Python to work with datasets to address popular and exciting riddles and problems. Overall, the course prepares students for the rest of their university life and the problems they may encounter throughout. |
| Course Contents: | • Logical and Critical Thinking
• Problem Decomposition • Pattern Recognition • Abstraction • Data types, forms and purposes • Introduction to Python • Algorithms and Data Analysis and Visualization |
| Learning Outcomes of the Course Unit (LO): |
|
| Planned Learning Activities and Teaching Methods: | 1 hour lecture, intended as an introduction to basic computational concepts, 1 hour groupwork on solving weekly problems, 1 hour presentation and discussion |
WEEKLY SUBJECTS AND RELATED PREPARATIONS
| Week | Subjects | Related Preperation |
|---|---|---|
| 1 | Introduction and Course Orientation | |
| 2 | Logical Thinking – Huseyin Sungur Kuyumcuoğlu | |
| 3 | Critical Thinking – Huseyin Sungur Kuyumcuoğlu | |
| 4 | Problem Decomposition – Sabri Gökmen | |
| 5 | Pattern Recognition – Sabri Gökmen | |
| 6 | Abstraction – Sabri Gökmen | |
| 7 | Introduction to Data – İpek İli | |
| 8 | Midterm I | |
| 9 | Introduction to Python I – Şebnem Eşsiz | |
| 10 | Introduction to Python II – Şebnem Eşsiz | |
| 11 | Fun with algorithms – Şebnem Eşsiz - Huseyin Sungur Kuyumcuoğlu | |
| 12 | Data Analysis - Ertunç Hünkar | |
| 13 | Data Visualization - Ertunç Hünkar | |
| 14 | Review and Dummy Final |
At Kadir Has University, a Semester is 14 weeks; The weeks 15 and 16 are reserved for final exams.
REQUIRED AND RECOMMENDED READING
| • Curzon, Paul, and Peter W. McOwan. The power of computational thinking: Games, magic and puzzles to help you become a computational thinker. World Scientific Publishing Company, 2017. • Riley, David, and Kenny A. Hunt. Computational thinking for the modern problem solver. Chapman and Hall/CRC, 2014. • Ferragina, Paolo, and Fabrizio Luccio. Computational Thinking: First Algorithms, Then Code. Springer, 2018. |
OTHER COURSE RESOURCES
| 1. R. Kowalski, Computational Logic and Human Thinking: How to be Artificially Intelligent Cambridge University Press; first edition (August 22, 2011). 2. M. Badger, Scratch 1.4: A Beginner’s Guide. Packt Publishing (July 17, 2009). 3. T. Gaddis, Starting Out with Alice: A Visual Introduction to Programming. Addison-Wesley, 2nd Edition(2010) 4. J. Zelle, Python Programming: An Introduction to Computer Science, Franklin, Beedle & Associates, Second edition (May 18, 2010) 5. S. Welch, From Idea to App: Creating iOS UI, animations, and gestures (Voices That Matter), New Riders Press (2011) 6. Appropriate articles from Communications of the ACM, IEEE Computer and IEEE Spectrum. (Approximately 1 article per 1-2 lectures). 7. Guzdial, Mark (2008). "Education: Paving the way for computational thinking". Communications of the ACM. 51 (8): 25 8. Edwin Kooge, Natasha Walk, and Peter C. Verhoef, (2016) Creating Value with Big Data Analytics: Making Smarter Marketing Decisions 9. http://people.scs.carleton.ca/~lanthier/teaching/ProcessingNotes |
ASSESSMENT METHODS AND CRITERIA
| Semester Requirements | Number | Percentage of Grade (%) |
|---|---|---|
| Laboratory | 10 | 60 |
| Midterms / Oral Exams / Quizes | 1 | 20 |
| Final Exam | 1 | 20 |
| Total: | 12 | 100 |
WORKLOAD
| Events | Count | Duration (Hours) | Total Workload (hour) |
|---|---|---|---|
| Course Hours | 14 | 1 | 14 |
| Laboratory | 12 | 2 | 24 |
| Extra-Class Activities (reading,individiual work, etc.) | 10 | 6 | 60 |
| Midterms / Oral Exams / Quizes | 2 | 9 | 18 |
| Final Exam | 1 | 9 | 9 |
| Total Workload (hour): | 125 | ||
1 ECTS = 25 Hours Workload
THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)
| # | PQ1 | PQ2 | PQ3 | PQ4 | PQ5 | PQ6 | PQ7 | PQ8 | PQ9 | PQ10 | PQ11 | PQ12 |
| LO1 | ||||||||||||
| LO2 | ||||||||||||
| LO3 | ||||||||||||
| LO4 | ||||||||||||
| LO5 |
Contribution: 1 Low, 2 Average, 3 High