Computer Science and Engineering (English) | |||||
PhD | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF: Level 8 |
School/Faculty/Institute | Graduate School | ||||||
Course Code | CSE 602 | ||||||
Course Title in English | Advanced Data Science and Engineering | ||||||
Course Title in Turkish | İleri Veri Bilimi ve Mühendisliği | ||||||
Language of Instruction | EN | ||||||
Type of Course | Flipped Classroom | ||||||
Level of Course | Advanced | ||||||
Semester | Spring | ||||||
Contact Hours per Week |
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Estimated Student Workload | 188 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 Doctorate Students | ||||||
Overall Educational Objective | To learn advanced data science and engineering methods and how to construct and implement intelligent systems on the big data set by new hybrid models. | ||||||
Course Description | This course is mainly designed to provide common advanced techniques in data science and engineering. This graduate course provides a comprehensive approach to big data analytics, exploratory data analysis, and enriched visualization, advanced machine learning as well as deep learning. The course content has a special emphasis on the mathematical foundations related to the commonly used algorithms in artificial intelligence, machine learning as well as deep learning. There will also be cases on practical applications including architectural decisions, applying the relevant algorithms, selecting suitable evaluative metrics for increasing model performance, and scalability. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Apply big data-based machine learning and AI algorithms to investigate patterns in data set 2) Apply advanced classification and clustering methods to predict future trend in big data 3) Assess the performance of the data science methods for big data 4) Design and construct hybrid data processing methods for big data 5) Communicate effectively by means of reports and presentations 6) Analyze and interpret data, and use analytic thinking to draw conclusions 7) Acquire and apply new knowledge as needed |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
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N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by |
Prepared by and Date | , |
Course Coordinator | İLKER BEKMEZCİ |
Semester | Spring |
Name of Instructor | Prof. Dr. İLKER BEKMEZCİ |
Week | Subject |
1) | Big data pre-processing and quality considerations |
2) | Computational complexity of data-intensive computing |
3) | Innovative methods for the design of the algorithms for solving big data problems |
4) | Representation, modeling, and visualization of big data |
5) | Storage, and management of big data |
6) | Data Mining methods and approaches for big data |
7) | Big databased machine learning |
8) | Big databased decision making |
9) | Statistical computation of big data |
10) | Graph-theoretic computation of big data |
11) | Optimization of big data |
12) | Novel applications in big data |
13) | Novel hybrid methods for big data |
14) | New approaches and new trends for big data |
15) | Final Examination Period |
16) | Final Examination Period |
Required/Recommended Readings | Journal of Data Science and Engineering, Springer | |||||||||||||||
Teaching Methods | Flipped learning is used as an instructional strategy. Students work individually for applied assignments. | |||||||||||||||
Homework and Projects | Assignments | |||||||||||||||
Laboratory Work | None | |||||||||||||||
Computer Use | Required | |||||||||||||||
Other Activities | None | |||||||||||||||
Assessment Methods |
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Course Administration |
karahocaa@mef.edu.tr 02123953600 Office hours: After the lecture hours Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. |
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 | 2 | 3 | 2 | 98 | ||
Laboratory | 7 | 0 | 3 | 1 | 28 | ||
Homework Assignments | 3 | 10 | 30 | ||||
Midterm(s) | 1 | 10 | 3 | 13 | |||
Final Examination | 1 | 16 | 3 | 19 | |||
Total Workload | 188 | ||||||
Total Workload/25 | 7.5 | ||||||
ECTS | 7.5 |