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| YZM3036 | Big Data Analysis | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Mustafa Hakan BOZKURT | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course covers prominent topics related to Big Data. The course aims to cover the characteristics and challenges of big data, big data platforms, storage and access frameworks for big data, machine learning algorithms and tools for big data analytics, data stream characteristics and real-time analytics on data streams, visualization of linked big data. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Define what big data is and its characteristics | | 1, | | LO - 2 : | Understand big data problems | | 1, | | LO - 3 : | Define the concepts of data extraction, transformation and loading | | 1, | | LO - 4 : | Will be able to comprehend general information about current big data technologies and will be able to make operations by using these technologies | | 1, | | LO - 5 : | Understand how to analyze big data streams | | 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 | | |
| What is big data? Introduction to big data. Current technologies needed and used for big data analysis. Extraction, storage, transformation, loading, labeling and discovery of big data. Analysis and visualization of big data. Machine learning in big data. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Big Data | | | Week 2 | Big Data Technologies | | | Week 3 | Big Data Technologies | | | Week 4 | Software Languages for Big Data | | | Week 5 | Data Models and Query Languages | | | Week 6 | Extract, Transform, Load | | | Week 7 | Extract, Transform, Load | | | Week 8 | Data Visualization | | | Week 9 | Midterm Exam | | | Week 10 | Big Data Analytics | | | Week 11 | Machine Learning with Big Data | | | Week 12 | Supervised Learning with Big Data | | | Week 13 | Unsupervised Learning with Big Data | | | Week 14 | Big Data Application Process Design | | | Week 15 | Big Data Application | | | Week 16 | Final Exam | | | |
| 1 | Chambers, B., & Zaharia, M. 2018; Spark: The definitive guide: Big data processing made simple. " O'Reilly Media, Inc.". | | | 2 | Balusamy, B., Kadry, S., & Gandomi, A. H. 2021;. Big Data: Concepts, Technology, and Architecture. John Wiley & Sons. | | | |
| 1 | McKinney W., 2022, Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, O?Reilly | | | 2 | Herend D. & Işık M. 2019; Adım Adım Bigdata ve Uygulamaları, Pusula Yayıncılık | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Project | 15 | | | 40 | | End-of-term exam | 16 | | 3 | 60 | | |
| 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 | 2 | 14 | 28 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Ödev | 2 | 9 | 18 | | Proje | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 1 | 14 | 14 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 105 |
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