| 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 | Standard Letter Grade | ||||||
| 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) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | |||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | |||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | |||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | |||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | |||||||
| 6) Internalization and dissemination of professional ethical standards. | |||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | |||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | |||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | |||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | |||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | |||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | |||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | ADEM KARAHOCA , March 2021 |
| Course Coordinator | TUBA AYHAN |
| Semester | Spring,Fall |
| Name of Instructor | Prof. Dr. ADEM KARAHOCA |
| 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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| 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 | ||||||