Information Package / Course Catalogue
| Computer Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | ||||||
| Course Code | COMP 466 | ||||||
| Course Title in English | Business Intelligence | ||||||
| Course Title in Turkish | İş Zekası | ||||||
| Language of Instruction | EN | ||||||
| Type of Course | Exercise,Flipped Classroom,Lecture | ||||||
| Level of Course | Intermediate | ||||||
| Semester | Spring,Fall | ||||||
| Contact Hours per Week |
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| Estimated Student Workload | 156 hours per semester | ||||||
| Number of Credits | 6 ECTS | ||||||
| Grading Mode | Standard Letter Grade | ||||||
| Pre-requisites |
COMP 109 - Computer Programming (JAVA) |
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| Expected Prior Knowledge | Basic programming and SQL knowledge | ||||||
| Co-requisites | None | ||||||
| Registration Restrictions | Only Undergraduate Students | ||||||
| Overall Educational Objective | To learn fundamentals of business intelligence concepts and construct basic data warehouse by using DMQL. | ||||||
| Course Description | The aim of this course is to provide the students with an understanding of how to getting 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 |
|---|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | |||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | |||||||
| 3) An ability to communicate effectively with a range of audiences | |||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | |||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | |||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | |||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,HW,Project |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | H | Project |
| 3) | An ability to communicate effectively with a range of audiences | S | Project |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | H | Exam,HW,Project |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | S | Project |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | H | HW,Lab,Project |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. | H | Project |
| Prepared by and Date | ADEM KARAHOCA , March 2021 |
| Course Coordinator | ADEM KARAHOCA |
| Semester | Spring,Fall |
| Name of Instructor | Öğr. Gör. KORAY KOCABAŞ |
Course Contents
| 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) | Intro to WEKA Tool |
| 11) | Preparation data set for Weka |
| 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 | |||||||||||||||
| Laboratory Work | None | |||||||||||||||
| Computer Use | Required | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
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| Course Administration |
karahocaa@mef.edu.tr Instructor’s office: 5th floor Phone number: 0 212 395 37 45 Office hours: After the lecture hours. E-mail address: karahocaa@mef.edu.tr Rules for attendance: No attendance required. Statement on plagiarism: YÖK Regulations |
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ECTS Student Workload Estimation
| 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 | 2 | 42 | |||
| Laboratory | 10 | 1 | 2 | 30 | |||
| Study Hours Out of Class | 1 | 1 | 10 | 11 | |||
| Project | 1 | 5 | 25 | 30 | |||
| Homework Assignments | 10 | 1 | 2 | 30 | |||
| Final Examination | 1 | 10 | 3 | 13 | |||
| Total Workload | 156 | ||||||
| Total Workload/25 | 6.2 | ||||||
| ECTS | 6 | ||||||