| School/Faculty/Institute | Graduate School | ||||||
| Course Code | CSE 616 | ||||||
| Course Title in English | Advanced Business Intelligence | ||||||
| Course Title in Turkish | İleri İş Zekası | ||||||
| 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 | 187 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 Doctorate Students | ||||||
| Overall Educational Objective | To learn and apply how to generate business intelligence using bulk data. | ||||||
| Course Description | The aim of this course is to provide the students with an understanding of how to get insight using bulk data. Querying, data warehouse design, understanding schemas, reporting layer and data visualization will be completed and the information about the end-to-end solution will be transferred. | ||||||
| Course Description in Turkish | Bu dersin amacı, öğrencilere toplu verileri kullanarak nasıl öngörü elde edeceklerini anlamalarını sağlamaktır. Sorgulama, veri ambarı tasarımı, şemaları anlama, raporlama katmanı, veri madenciliği ve veri görselleştirme tamamlanacak ve uçtan uca çözümle ilgili bilgiler aktarılacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Identify, formulate, and solve business intelligence problems by applying principles of engineering as well as science and mathematics 2) Communicate effectively with a range of audiences via the lab reports and project presentations 3) Recognize ethical and professional responsibilities in engineering situations that are directly related to artificial intelligence and related technologies while considering the impact of engineering solutions in global, economic, environmental, and societal contexts 4) Function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives 5) Develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions for the given cases related to business intelligence 6) Acquire and apply contemporary issues and methods in business intelligence and data mining with using appropriate learning strategies 7) Develop a full cycle business intelligence and data mining application |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by |
| Prepared by and Date | , |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Spring |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
| Week | Subject |
| 1) | Business Intelligence |
| 2) | Data Warehousing |
| 3) | RDBMS Concepts I |
| 4) | RDBMS Concepts II |
| 5) | Modeling the Dimensions and Creating the Aggregations |
| 6) | Designing Data Warehouse |
| 7) | Introduction to Data Mining |
| 8) | Unsupervised Methods |
| 9) | Supervised Methods |
| 10) | Semi-supervised Methods |
| 11) | Preparation of data set |
| 12) | Real life BI and data mining applications |
| 13) | Project Presentations |
| 14) | Project Presentations |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | 1. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th edition, ISBN 978-0-13-463328-2, by Ramesh Sharda, Dursun Delen, and Efraim Turban, Pearson Education,2018 2. Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition, Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal | ||||||||||||
| Teaching Methods | Flipped classroom. Students will work individually for assignments. | ||||||||||||
| Homework and Projects | Assignments & Project | ||||||||||||
| Laboratory Work | None | ||||||||||||
| Computer Use | Required | ||||||||||||
| Other Activities | None | ||||||||||||
| Assessment Methods |
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| Course Administration |
cakart@mef.edu.tr 02123953600 Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. |
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| 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 | ||
| Homework Assignments | 6 | 10 | 1 | 66 | |||
| Final Examination | 1 | 20 | 2 | 1 | 23 | ||
| Total Workload | 187 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||