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| YBS3010 | Big Data and Management | 3+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Fatih GÜRCAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course aims to provide competencies in analytical data production, storage, management, transfer and in-depth analysis of data by using existing technologies, tools, architectures and systems in the field of Big Data. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Explain the concept of big data | 1,2,4,8 | 1 | | LO - 2 : | Explain how big data is obtained | 1,2,4,8 | 1 | | LO - 3 : | Know the methods of storage of big data | 1,2,4,8 | 1 | | LO - 4 : | Know big data systems (Hadoop, MapReduce, Spark) | 1,2,4,8 | 1 | | LO - 5 : | Know how to analyze big data | 1,2,4,8 | 1 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Fundamentals of big data, big data collection, big data storage, big data systems, big data analysis, Hadoop, MapReduce, large-scale supervised machine learning, data streams, clustering, recommendation systems, NoSQL systems (Cassandra, Pig, Hive) and applications, sample applications |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basics of big data | | | Week 2 | Big Data Collection: Data Cleaning and Integration | | | Week 3 | Big Data Collection:Hosted Data Platforms and the Cloud
| | | Week 4 | Big Data Collection: Modern Databases
| | | Week 5 | Big Data Storage: Distributed Computing Platforms | | | Week 6 | Big Data Storage: NoSQL | | | Week 7 | Big Data Systems: Security | | | Week 8 | Big Data Systems (Hadoop, MapReduce, Spark)-I | | | Week 9 | Midterm exam | | | Week 10 | Big Data Systems (Hadoop, MapReduce, Spark)-II | | | Week 11 | Big Data Analytics: Algorithms-I | | | Week 12 | Big Data Analytics: Algorithms-II | | | Week 13 | Big Data Analytics: Data Compression | | | Week 14 | Big Data Analytics: Machine Learning Tools | | | Week 15 | Big data analysis applications: Medicine and Finance | | | Week 16 | Final exam | | | |
| 1 | Akpınar, Haldun. 2014. Data Veri Madenciliği - Veri Analizi (2. Baskı). Papatya Bilim. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 50 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 1 | 1 | 1 | | Uygulama | 2 | 14 | 28 | | Dönem sonu sınavı için hazırlık | 2 | 6 | 12 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 142 |
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