İş Analitiği

İçerik

Dersin Amacı

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.

Dersin İçeriği

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

Dersin Öğrenme Çıktıları

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.

Öğretim Yöntemleri

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.

Kaynaklar

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.

Konu Başlıkları

Değerlendirme Sistemi

Yeterlilik İlişkisi

AKTS - İş Yükü Tablosu