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 |
School/Faculty/Institute | Gradutate School of Science and Engineering | ||||
Course Code | BDA 591 | ||||
Course Title in English | Project | ||||
Course Title in Turkish | Project | ||||
Language of Instruction | EN | ||||
Type of Course | Project | ||||
Level of Course | Introductory | ||||
Semester | Spring,Fall,Summer School | ||||
Contact Hours per Week |
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Estimated Student Workload | 308 hours per semester | ||||
Number of Credits | 10 ECTS | ||||
Grading Mode | Standard Letter Grade | ||||
Pre-requisites |
BDA 589 - Project Proposal |
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Expected Prior Knowledge | BDA 589 Proje Önerisi | ||||
Co-requisites | None | ||||
Registration Restrictions | Only Graduate Students | ||||
Overall Educational Objective | To learn how to conduct a project individually. | ||||
Course Description | The aim of the course is to learn the basics of how to conduct a research-based project | ||||
Course Description in Turkish | Bu dersin amacı, araştırma-temelli bir projenin nasıl yapılacağını öğretmesidir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Provide a through out literature survey 2) Develop a project framework and methodology 3) Analyze the obtained data and provide a sufficient analysis 4) Evaluate the findings in adequate details. |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
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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. |
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. | H | Project |
2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | H | Project |
3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | H | Project |
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. | H | Project |
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. | H | Project |
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 | Project |
7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | S | Project |
8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | N | Project |
9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | H | Project |
10) | Understanding of social and environmental aspects of machine learning applications. | N | Project |
Prepared by and Date | , |
Course Coordinator | ÖZGÜR ÖZLÜK |
Semester | Spring,Fall,Summer School |
Name of Instructor | Prof. Dr. ÖZGÜR ÖZLÜK |
Week | Subject |
1) | Introduction |
2) | Introduction |
3) | Fundamental Concepts in Project Format I |
4) | Fundamental Concepts in Project Format II |
5) | Literature Search and Survey I |
6) | Literature Search and Survey II |
7) | Designing the Research Framework I |
8) | Designing the Research Framework II |
9) | Applying Methodology I |
10) | Applying Methodology II |
11) | Outputs and Findings I |
12) | Outputs and Findings II |
13) | Potential Conclusions and Alternative Approaches I |
14) | Potential Conclusions and Alternative Approaches II |
15) | Final Examination Period |
16) | Final Examination Period |
Required/Recommended Readings | None | |||||||||
Teaching Methods | Flipped classroom. Students will work for projects. | |||||||||
Homework and Projects | Project | |||||||||
Laboratory Work | None | |||||||||
Computer Use | Students will apply the methods they learned | |||||||||
Other Activities | None | |||||||||
Assessment Methods |
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Course Administration |
02123953600 |
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 | 5 | 1 | 98 | ||
Paper Submission | 14 | 14 | 1 | 210 | |||
Total Workload | 308 | ||||||
Total Workload/25 | 12.3 | ||||||
ECTS | 10 |