| School/Faculty/Institute | Graduate School | ||||||
| Course Code | ITC 537 | ||||||
| Course Title in English | Business Intelligence | ||||||
| Course Title in Turkish | İş Zekası | ||||||
| Language of Instruction | EN | ||||||
| Type of Course | Flipped Classroom | ||||||
| Level of Course | Intermediate | ||||||
| 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 | Subject fields without course codes | ||||||
| Co-requisites | None | ||||||
| Registration Restrictions | Only Graduate 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) Bilim ve matematiğin yanı sıra mühendislik ilkelerini de uygulayarak iş zekası problemlerini tanımlamak, formüle etmek ve çözmek 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 |
|---|---|---|---|---|---|---|---|
| 1) An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | |||||||
| 2) An ability to apply scientific and practical knowledge in statistics, computing and computer science. | |||||||
| 3) A Comprehensive knowledge of analysis and modeling methods and their limitations. | |||||||
| 4) An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | |||||||
| 5) An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | |||||||
| 6) An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | |||||||
| 7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | |||||||
| 8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | |||||||
| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | |||||||
| 10) An understanding the social and environmental aspects of IT applications. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | N | |
| 2) | An ability to apply scientific and practical knowledge in statistics, computing and computer science. | N | |
| 3) | A Comprehensive knowledge of analysis and modeling methods and their limitations. | N | |
| 4) | An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | N | |
| 5) | An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | N | |
| 6) | An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | N | |
| 7) | An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | N | |
| 8) | An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | N | |
| 9) | An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | N | |
| 10) | An understanding the social and environmental aspects of IT applications. | N |
| Prepared by and Date | , |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Spring |
| Name of Instructor |
| Week | Subject |
| 1) | Introduction to 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 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 |
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 | ||
| Project | 6 | 10 | 1 | 66 | |||
| Final Examination | 1 | 20 | 2 | 1 | 23 | ||
| Total Workload | 187 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||