| School/Faculty/Institute |
Gradutate School of Science and Engineering |
| Course Code |
BDA 507 |
| Course Title in English |
Introduction to Computer Programming (Python) |
| Course Title in Turkish |
Büyük Veri için Programlamaya Giriş (Python) |
| Language of Instruction |
EN |
| Type of Course |
Exercise,Flipped Classroom,Lecture |
| Level of Course |
Intermediate |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: |
Lab: |
Other: |
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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 Competences
Upon 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) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
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| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
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| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
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| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
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| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
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| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
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| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
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| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
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| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
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| 10) Understanding of social and environmental aspects of machine learning applications. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
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| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
S |
|
| 2) |
Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
H |
|
| 3) |
Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
H |
|
| 4) |
Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
N |
|
| 5) |
Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
N |
|
| 6) |
Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
N |
|
| 7) |
Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
N |
|
| 8) |
Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
N |
|
| 9) |
Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
N |
|
| 10) |
Understanding of social and environmental aspects of machine learning 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 Presentation |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue