ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | DATS5027 | ||||
| Course Name: | Machine Learning Applications in Business | ||||
| Semester: | Spring | ||||
| 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. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): |
Prof. Dr. DURSUN DELEN |
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| Course Assistants: |
Course Objective and Content
| Course Objectives: | The purpose of this course is to provide students with a comprehensive understanding of how machine learning can be leveraged within a business setting. Students will be exposed to practical applications of machine learning algorithms and will learn to implement these techniques to solve real-world business problems. By the end of the course, students will be proficient in selecting appropriate machine learning methods for various business scenarios, optimizing model performance, and translating quantitative findings into strategic business decisions. |
| Course Content: | 1. Overview of machine learning concepts and algorithms in a business context. 2. Data preprocessing, feature engineering, and data visualization techniques. 3. Supervised learning models for regression and classification in business decision-making. 4. Unsupervised learning for customer segmentation, market basket analysis, and anomaly detection. 5. Evaluation of machine learning models and deployment strategies for business applications. |
Learning Outcomes
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The students who have succeeded in this course;
1) Critically analyze business problems to identify opportunities for machine learning solutions. 2) Effectively apply data preprocessing and feature engineering to prepare datasets for machine learning. 3) Develop and tune machine learning models using contemporary software tools. 4) Interpret and communicate the results of machine learning models to a business audience. 5) Design and implement machine learning model deployment strategies within a business infrastructure. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Machine Learning and its business relevance. | |
| 2) | Data collection, preprocessing, and visualization techniques. | |
| 3) | Exploring linear regression models and their business applications. | |
| 4) | Classification models for customer prediction and retention strategies. | |
| 5) | Decision trees and ensemble methods for complex business decisions. | |
| 6) | Unsupervised learning techniques for market segmentation. | |
| 7) | Model selection, cross-validation, and hyperparameter tuning. | |
| 8) | Midterm Exam. | |
| 9) | Neural networks and deep learning in business applications. | |
| 10) | Natural Language Processing (NLP) for customer feedback analysis. | |
| 11) | Reinforcement learning for dynamic business strategy development. | |
| 12) | Model evaluation metrics and model interpretability. | |
| 13) | Ethical considerations and bias mitigation in business machine learning. | |
| 14) | Deployment of models into production, monitoring, and maintenance. | |
| 15) | Case studies of successful machine learning applications in business. | |
| 16) | Final Exam. |
Sources
| 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 - Program Learning Outcome Relationship
| 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. | 3 | 3 | 3 | 3 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 | 3 | 3 | 3 | 3 |
| 3) Students who complete this program; Recognize the mathematics and code in application processes | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 | 3 | 3 | 3 | 3 |
| 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. | 3 | 3 | 3 | 3 | 3 |
Course - Learning Outcome Relationship
| 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. | 3 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognize the mathematics and code in application processes | 3 |
| 4) | Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 |
| 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. | 3 |
Assessment & Grading
| 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 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 14 | 0 | 3 | 42 | |||
| Midterms | 1 | 40 | 1 | 41 | |||
| Final | 1 | 60 | 1 | 61 | |||
| Total Workload | 144 | ||||||