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 589 | ||||
Course Title in English | Project Proposal | ||||
Course Title in Turkish | Project Proposal | ||||
Language of Instruction | EN | ||||
Type of Course | Project | ||||
Level of Course | Introductory | ||||
Semester | Spring | ||||
Contact Hours per Week |
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Estimated Student Workload | 64 hours per semester | ||||
Number of Credits | 2.5 ECTS | ||||
Grading Mode | Pass / Fail | ||||
Pre-requisites | None | ||||
Expected Prior Knowledge | None | ||||
Co-requisites | None | ||||
Registration Restrictions | Only Graduate Students | ||||
Overall Educational Objective | To learn how to develop a project proposal and to develop it for the Capstone project. | ||||
Course Description | The aim of the course is to learn the basics of how to propose a project and to develop it. | ||||
Course Description in Turkish | Bu dersin amacı, proje önerisinin nasıl yapılacağı ve projenin nasıl geliştirileceğini öğretmektir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Recognize and discuss the fundamental stages in project proposal 2) Organize and develop a project proposal |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 |
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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. | S | |
2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | S | |
3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | S | |
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. | S | |
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. | S | |
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. | S | |
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. | H | |
10) | Understanding of social and environmental aspects of machine learning applications. | N |
Prepared by and Date | , |
Course Coordinator | ÖZGÜR ÖZLÜK |
Semester | Spring |
Name of Instructor | Prof. Dr. ÖZGÜR ÖZLÜK |
Week | Subject |
1) | Intro to Project Proposal I |
2) | Intro to Project Proposal II |
3) | Fundamental Concepts in Project Design and Development I |
4) | Fundamental Concepts in Project Design and Development II |
5) | Literature Search and Survey I |
6) | Literature Search and Survey II |
7) | Defining Research Questions and Scope I |
8) | Defining Research Questions and Scope II |
9) | Determining Methodology I |
10) | Determining Methodology II |
11) | Expected Outputs and Findings I |
12) | Expected 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 | Each week there will be a lab session | |||||||||
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 | 1 | 1 | 42 | ||
Paper Submission | 1 | 0 | 22 | 22 | |||
Total Workload | 64 | ||||||
Total Workload/25 | 2.6 | ||||||
ECTS | 2.5 |