Information Package / Course Catalogue
| Information Technologies (English) (Thesis) | |||||
| Master | Length of the Programme: 2 | Number of Credits: 120 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Graduate School | ||||
| Course Code | ITC 507 | ||||
| Course Title in English | Programming for Data Science | ||||
| Course Title in Turkish | Programming for Data Science | ||||
| Language of Instruction | EN | ||||
| Type of Course | Exercise,Lecture | ||||
| Level of Course | Intermediate | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 174 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | Students should be able to understand fundamentals of computer programming and learn how to design and implement computer algorithms to solve basic engineering problems in Python programming language | ||||
| Course Description | Fundamentals of computer programming. Algorithm development using iterative refinement, structural design, I/O processes, sequential processes, decision making processes, recursive processes, functions, arrays, files, formatted I/Os, programs in Python | ||||
| Course Description in Turkish | Bilgisayar programlamanin temelleri. Yapisal tasarim, iterative programlama, girdi/cikti yontemleri, karar yapilari, fonksiyon, katar, dosya kavramlarini kullanarak algoritma tasarimi ve gelistirilmesi. Programlama kavramlarinin Python dili kullanilarak ogretilmesi. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand computer programming fundamentals.(sequence, branching, iteration) 2) Design basic computer algorithms 3) Create computer programs to solve engineering problems (Implementation in Python) 4) Understand basics of C programming language ( functions, arrays, syntax of Python) |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | ||||
| 2) An ability to apply scientific and practical knowledge in statistics, computing and computer science. | ||||
| 3) A Comprehensive knowledge of analysis and modeling methods and their limitations. | ||||
| 4) An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | ||||
| 5) An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | ||||
| 6) An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | ||||
| 7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | ||||
| 8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | ||||
| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | ||||
| 10) An understanding the social and environmental aspects of IT applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | N | |
| 2) | An ability to apply scientific and practical knowledge in statistics, computing and computer science. | N | |
| 3) | A Comprehensive knowledge of analysis and modeling methods and their limitations. | N | |
| 4) | An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | N | |
| 5) | An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | N | |
| 6) | An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | N | |
| 7) | An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | N | |
| 8) | An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | N | |
| 9) | An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | N | |
| 10) | An understanding the social and environmental aspects of IT applications. | N |
| Prepared by and Date | , |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | Introduction to programming |
| 2) | Variables, strings, numbers, expressions |
| 3) | Sequence, Conditions, loops |
| 4) | Sequence, Conditions, loops |
| 5) | Algorithm Pseudocode |
| 6) | List and list operations |
| 7) | Data structures |
| 8) | Data structures |
| 9) | Data structures |
| 10) | Hash function |
| 11) | Recursive procedures |
| 12) | Open source and big data with python |
| 13) | Open source and big data with python |
| 14) | Students Presentations |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | Python Programming (open source) wikibooks, https://en.wikibooks.org/wiki/Python_Programming | ||||||||||||
| Teaching Methods | Lecturing in the class. Students work individually for project | ||||||||||||
| Homework and Projects | 3 Homeworks, 1 Project | ||||||||||||
| Laboratory Work | Programming in computer laboratory | ||||||||||||
| Computer Use | For Programming with Python | ||||||||||||
| Other Activities | None | ||||||||||||
| Assessment Methods |
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| Course Administration |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 0.5 | 63 | ||
| Laboratory | 14 | 1 | 2 | 0.5 | 49 | ||
| Homework Assignments | 4 | 2 | 10 | 1 | 52 | ||
| Midterm(s) | 1 | 1 | 2 | 3 | |||
| Final Examination | 1 | 20 | 1 | 21 | |||
| Total Workload | 188 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||