| Management Information System (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | MIS5001 | ||||
| Course Name: | Data Science and Managing Big Data | ||||
| Semester: |
Fall Spring |
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| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Doç. Dr. OKAN YAŞAR | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
| Course Objectives: | This course is designed to provide students with a comprehensive understanding of data science with a particular focus on managing big data. Students will learn to apply data science techniques to analyze large and complex datasets and gain insights that can inform business decision-making. The coursework covers the full spectrum of data science, including data acquisition, cleaning, analysis, and representation, as well as the tools and technologies used to handle big data in a scalable way. The ethical implications of data science practices will also be examined. |
| Course Content: | 1. Fundamental concepts of data science and the data analytics lifecycle. 2. Managing, cleaning, and processing big data sets with modern tools. 3. Statistical analysis and predictive modeling techniques for big data. 4. Big data storage, cloud infrastructure, and database technologies. 5. Ethical considerations and data privacy issues in handling big data. |
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The students who have succeeded in this course;
1) Mastery of the concepts and tools necessary for managing and analysing big data. 2) Ability to perform statistical and predictive analyses on large datasets. 3) Understanding of big data technologies including storage, processing, and cloud-based solutions. 4) Knowledge of the ethical issues surrounding big data analytics and privacy concerns. 5) Preparedness for applying data science methods in a business or research environment. |
| Week | Subject | Related Preparation |
| 1) | Introduction to Data Science and its role in the modern data landscape. | |
| 2) | Data structures, types, and sources for big data analytics. | |
| 3) | Data cleaning, preparation, and pre-processing techniques. | |
| 4) | Descriptive statistics and exploratory data analysis for big data. | |
| 5) | Statistical inference and hypothesis testing in high-dimensional spaces. | |
| 6) | Introduction to machine learning and predictive modeling. | |
| 7) | Data visualization techniques for large and complex data sets. | |
| 8) | Midterm Exam | |
| 9) | Big data storage solutions and database technologies. | |
| 10) | Cloud computing for big data: services, tools, and architectures. | |
| 11) | Scalability and parallel processing with big data tools. | |
| 12) | Advanced machine learning models for big data analytics. | |
| 13) | Data privacy, security, and ethical considerations. | |
| 14) | Application of data science in industry case studies. | |
| 15) | Preparing for the final project: an end-to-end data science analysis. | |
| 16) | Final Exam |
| Course Notes / Textbooks: | Herhangi bir ders kitabı bulunmamaktadır. There is no textbook. |
| References: | Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used. |
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) - Knows management information systems and their strategic importance. | 3 | 2 | 2 | 2 | 3 |
| 2) - Recognizes all the elements and concepts in the design of the management information system. | 3 | 3 | 3 | 3 | 3 |
| 3) - Able to explain the relationship between MIS and the new industrial revolution. | 3 | 3 | 3 | 3 | 3 |
| 4) - Understands the role of management information system in solving problems and gaining competitive advantage. | 3 | 3 | 2 | 3 | 3 |
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | - Knows management information systems and their strategic importance. | 3 |
| 2) | - Recognizes all the elements and concepts in the design of the management information system. | 3 |
| 3) | - Able to explain the relationship between MIS and the new industrial revolution. | 3 |
| 4) | - Understands the role of management information system in solving problems and gaining competitive advantage. | 3 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 3 | 9 |
| Field Work | 1 | 3 |
| Midterms | 16 | 82 |
| Final | 16 | 48 |
| Total Workload | 142 | |