| Data Science (Master) (without Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | VB5019 | ||||
| Course Name: | Data Science and Artificial Intelligence Applications in Business | ||||
| Semester: |
Fall Spring |
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| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | Şebnem Özdemir | ||||
| Course Assistants: |
| Course Objectives: | The aim of this course is to provide students with the basic principles, methods and potential benefits of data science and artificial intelligence applications in business processes and to enable businesses to make data-driven decisions and use artificial intelligence technologies ethically and safely. |
| Course Content: | 1. Basic principles and methods of data science and artificial intelligence concepts. 2. Data collection, data cleaning and data analysis processes in businesses. 3. Usage areas and potential benefits of artificial intelligence applications in business. 4. Application of machine learning and deep learning algorithms to business problems. 5. Data security and ethical issues in business. |
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The students who have succeeded in this course;
1) 1. Students will understand the basic concepts and methods of data science and artificial intelligence applications in business. 2) 2. Students will be able to apply data collection, data cleaning and data analysis processes in business. 3) 3. Students will be able to evaluate the use cases and potential benefits of AI applications in business. 4) 4. Students will be able to apply machine learning and deep learning algorithms to business problems. 5) 5. Students will understand data security and ethical issues in business and gain the ability to use AI technologies in an ethical and secure manner. |
| Week | Subject | Related Preparation |
| 1) | 1. Introduction and Data Science Basics | No Preliminary Preparation Required. |
| 2) | 2. Data Collection and Data Cleaning | No Preliminary Preparation Required. |
| 3) | 3. Data Analysis and Visualization | No Preliminary Preparation Required. |
| 4) | 4. Machine Learning Basics | No Preliminary Preparation Required. |
| 5) | 5. Supervised Learning Algorithms | No Preliminary Preparation Required. |
| 6) | 6. Unsupervised Learning Algorithms | No Preliminary Preparation Required. |
| 7) | 7. Deep Learning and Neural Network Concepts | No Preliminary Preparation Required. |
| 8) | 8. Midterm Exam | No Preliminary Preparation Required. |
| 9) | 9. Artificial Intelligence Applications in Businesses | No Preliminary Preparation Required. |
| 10) | 10. Natural Language Processing and Text Mining | No Preliminary Preparation Required. |
| 11) | 11. Advanced Machine Learning Techniques | No Preliminary Preparation Required. |
| 12) | 12. Data Security and Ethics | No Preliminary Preparation Required. |
| 13) | 13. Big Data and Database Management Fundamentals | No Preliminary Preparation Required. |
| 14) | 14. Pattern Recognition and Basic Image Processing | No Preliminary Preparation Required. |
| 15) | 15. The Future of Artificial Intelligence Applications in Business | No Preliminary Preparation Required. |
| 16) | 16. Final Exam | No Preliminary Preparation Required. |
| Course Notes / Textbooks: | James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with Applications in R. Springer. |
| References: | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. |
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | |||||
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | |||||
| 3) Students who complete this program; Recognizes mathematics and code in application processes. | |||||
| 4) Students who complete this program; Explain the effects of processes in data science on output and individual. | |||||
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | |||||
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | |
| 3) | Students who complete this program; Recognizes mathematics and code in application processes. | |
| 4) | Students who complete this program; Explain the effects of processes in data science on output and individual. | |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 50 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 28 |
| Midterms | 7 | 52 |
| Final | 8 | 68 |
| Total Workload | 148 | |