(Mİ628)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Mİ628 | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | Turkish |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Deniz UZTÜRK BARAN uzturkdeniz@gmail.com (Email) |
| Assistant | |
| Objective | The aim of this course is to equip students with the analytical skills required to make data-driven decisions in business environments. The course introduces key concepts in data science, including descriptive, predictive, and prescriptive analytics, alongside optimization techniques and big data technologies. By integrating real-world examples, business tools, and ethical considerations, the course aims to build analytical literacy and foster strategic thinking in the age of digital transformation. |
| Content |
Week 1 Review on Data and Business Data and AI Week 2 Introduction to Business Analytics and Data Ethics and Assignments of Semester-Beginning Presentations Week 3 Presentation of Semester-Beginning Assignments – Introduction to Excel and Descriptive Analytics (1) Week 4 Descriptive Analytics and Applications (2) Week 5 Predictive Analytics (1) Week 6 Predictive Analytics and Applications (2): Using AI for predictive Analytics Week 7 Wrap-up for midterm exam and Case Studies Week 8 MIDTERM EXAM (Final Project Topics will be provided) Week 9 Prescriptive Analytics (1) Week 10 Prescriptive Analytics and Applications (2) Week 11 Linear Optimization and Decision Analysis Week 12 Nonlinear Optimization and Decision Analysis Week 13 Big Data Technologies and Analytics /course wrap-up and Case Studies Week 14 Final Project Presentations |
| Course Learning Outcomes |
Upon successful completion of this course, students will be able to: 1. Define business analytics and explain its components and importance in modern businesses. 2. Analyze data-driven decision-making processes and evaluate real-world business cases. 3. Apply descriptive analytics techniques (e.g., data visualization, summary statistics) using tools such as Excel. 4. Understand and interpret predictive analytics methods including regression, time-series analysis, and machine learning applications. 5. Utilize prescriptive analytics techniques to optimize business strategies and decisions. 6. Employ linear and nonlinear optimization methods for solving resource allocation and planning problems. 7. Demonstrate awareness of current issues in data ethics, data privacy, and the use of big data in business. 8. Strengthen problem-solving and analytical thinking skills through case studies and project-based learning. |
| Teaching and Learning Methods | The course is delivered through a mix of theoretical lectures, practical lab sessions (Excel, R), case study discussions, poster presentations, and project-based assessments. Students are encouraged to engage actively in class by presenting assigned topics and developing data-driven solutions to real-world problems. The teaching strategy emphasizes experiential learning, combining foundational theory with modern applications in various industries, including marketing, finance, healthcare, and logistics. |
| References |
Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2024). Business analytics: Descriptive, predictive, prescriptive. Cengage Learning. Provost, Foster, and Tom Fawcett. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, 2013. Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, 2013. Readings and case studies will be provided throughout the course. The beginning and end-of-semester assignments are mandatory and must be completed to pass the course. |
Theory Topics
| Week | Weekly Contents |
|---|
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 3 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 4 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 1 | 10 |
| Presentation | 1 | 20 |
| Midterm Examinations (including preparation) | 1 | 30 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 3 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | The student will be able to use the knowledge and skills needed for upper echelons of management. | X | ||||
| 2 | The student will be able to identify the behaviors to minimize the risks by taking into account internal and external conditions that effect the organization. | X | ||||
| 3 | The student should have the general knowledge about labor law, sociology, psychology, organizational sociology, logistics and quantitative methods that can be needed out of basic management functions. | X | ||||
| 4 | The student should have a command of information and communication technologies that are needed for professional business life. | X | ||||
| 5 | The student should keep the competitive power of business at higher levels in the increasing globalization and competition conditions with following visionary applications. | X | ||||
| 6 | The student will be able to identify the changes that are in the internal and external environment of business and the problems which are caused by these changes in time. Also, the student should improve solutions for these problems and apply them. | X | ||||
| 7 | The student will be able to generate the solutions for managerial problems with the help of management games and simulation techniques. | X | ||||
| 8 | The student should make presentations and discuss managerial problems at least two foreign languages. | X | ||||
| 9 | The student will be able to tend towards practices of case study and sample event assessment and contribute the projects in which he/she serves. | X | ||||
| 10 | The student should get the responsibility in teamwork and contribute to team work. | X | ||||
| 11 | The student should get the habit of updating his technical skills to adjust the changing and improving scientific and technologic environment. | X | ||||
| 12 | The student should make a principle of behaving ethical in business life and guarding the interests of his colleagues and business stakeholders. | X | ||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 14 | 2 | 28 |
| Assignments | 1 | 15 | 15 |
| Presentation | 1 | 5 | 5 |
| Midterm Examinations (including preparation) | 1 | 15 | 15 |
| Project | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Total Workload | 125 | ||
| Total Workload / 25 | 5.00 | ||
| Credits ECTS | 5 | ||