| School/Faculty/Institute | Graduate School | ||||
| Course Code | ITC 543 | ||||
| Course Title in English | Applications in Big Data Management | ||||
| Course Title in Turkish | Applications in Big Data Management | ||||
| 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 | 174 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 Graduate Students | ||||
| Overall Educational Objective | To learn the basic designing data warehouse and big data systems. | ||||
| Course Description | The aim of this course is to provide the students with an understanding of how to getting insight using big data. Querying, data warehouse design, understanding schemas, reporting layer and data visualization, and big data ecosystem will be completed and the information about the end-to-end solution will be transferred. | ||||
| Course Description in Turkish | Bu dersin amacı öğrencilere büyük veriyi kullanarak nasıl öngörü elde edeceklerini anlamalarını sağlamaktır. Sorgulama, veri ambarı tasarımı, şemaları anlama, raporlama katmanı ve veri görselleştirme ve büyük veri ekosistemi 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) Design and querying OLTP Systems. 2) Design and querying OLAP Systems. 3) Designing modern data warehouse architecture. 4) Execution on real world problems and data systems. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 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 | İLKER BEKMEZCİ |
| Semester | Spring |
| Name of Instructor | Prof. Dr. İLKER BEKMEZCİ |
| Week | Subject |
| 1) | Introduction to Big Data |
| 2) | Statistics and Exploratory Data Analysis |
| 3) | Business Intelligence: OLAP, Data Warehouse, and Column Store |
| 4) | Introduction to Data Mining |
| 5) | Unsupervised Methods |
| 6) | Supervised Methods |
| 7) | Intro to WEKA Tool |
| 8) | Preparation data set for Weka |
| 9) | Machine Learning: Clustering (Unsupervised Learning) |
| 10) | Machine Learning: Classification (Supervised Learning) |
| 11) | Machine Learning & MapReduce |
| 12) | Graph Algorithms & MapReduce |
| 13) | Final Project Presentations |
| 14) | Final 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, Quizzes & 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 | 1.5 | 49 | |||
| Laboratory | 14 | 2 | 1.5 | 49 | |||
| Homework Assignments | 9 | 2 | 1 | 27 | |||
| Midterm(s) | 1 | 30 | 30 | ||||
| Final Examination | 1 | 30 | 3 | 33 | |||
| Total Workload | 188 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||