Business Analytics(G318)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| G318 | Business Analytics | 6 | 3 | 0 | 0 | 3 | 5 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Bachelor 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 acquisition of high-level knowledge of the functions of production management and marketing, management, accounting and finance; the skill of using this knowledge. | X | ||||
| 2 | The acquisition of basic conceptual knowledge about scientific fields related to professional life, such as law, economics, sociology, psychology, social psychology and quantitative methods. | X | ||||
| 3 | The ability to work effectively in a team; the ability to pass on knowledge to other members of the team. | X | ||||
| 4 | The competence to use his/her knowledge on the field, to analyze and interpret the data obtained, to identify the problems encountered, to provide appropriate solutions and scientifically defend these solution suggestions when necessary. | X | ||||
| 5 | The competence to determine the aims and objectives of the company or institution in which he/she is employed by taking into account the needs, the competitive dynamics of the market and by calculating the risks incurred; the ability to engage in entrepreneurship and establish and manage a business. | X | ||||
| 6 | Awareness of constantly updating his/her professional knowledge and skills; the ability to adapt to change and innovation; the competence to evaluate critically the information he/she has acquired. | X | ||||
| 7 | Basic knowledge of information and communication technologies required for professional life; the ability to use core office programs at an advanced level; expertise in data processing and report writing in the IT environment. | X | ||||
| 8 | Ability to follow current information in his/her field in both English and French and to communicate in writing and verbally with stakeholders in both languages. | X | ||||
| 9 | The ability to conduct researches and studies taking into account the market, dynamics of competition, organizational and global factors and scientific methods; contribute to projects, take responsibility in projects, display competence to make innovative and effective decisions. | X | ||||
| 10 | Ability to develop strategies, find creative solutions to management problems by building relations with other areas of the social sciences and take the responsibility of these decisions. | X | ||||
| 11 | Consciousness of taking into account ethical values, when making decisions and being involved in business life. | X | ||||
| 12 | Awareness of the impact of practices related to his/her field on the global and social dimensions (universality of social rights, social justice, cultural values, environmental problems, sustainability, etc.) and their legal consequences. | 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 | ||