Information Package / Course Catalogue
| Big Data Analytics (English) (Non-Thesis) | |||||
| Master | Length of the Programme: 1.5 | Number of Credits: 90 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
ECTS Course Information Package
| School/Faculty/Institute | Graduate School | ||||
| 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,Ters-yüz öğrenme,Lecture | ||||
| Level of Course | Orta | ||||
| 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 | ||||
| Co-requisites | None | ||||
| Expected Prior Knowledge | 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 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) 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. | ||||
| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | ||||
| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | ||||
| 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. | ||||
| 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. | ||||
| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | ||||
| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | ||||
| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | ||||
| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | ||||
| 10) Understanding of social and environmental aspects of machine learning applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| 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 |
| Required/Recommended Readings | Python Programming (open source) wikibooks, https://en.wikibooks.org/wiki/Python_Programming | ||||||||||||
| Teaching Methods | Lecturing in the class. Students will 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 |
cakart@mef.edu.tr 02123953600 |
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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 | ||||||