UGİ383

School of Economics and Administrative Sciences \ International Entrepreneurship

3

6

Elective

Turkish

-

All Departments

This course aims to introduce the application of big data, artificial intelligence, and machine learning techniques in the decision-making processes of businesses. It is designed to help students understand these technologies and gain practical skills.

(i) Pollock III, P. H., & Edwards, B. C. (2019). The Essentials of Political Analysis. CQ Press.(ii) Pollock III, P. H., & Edwards, B. C. (2018). A Stata® Companion to Political Analysis. CQ Press.(iii) Hamilton, L. C. (2012). Statistics with Stata: Version 12. Cengage Learning.(iv) Mitchell, M. N. (2008). A Visual Guide to Stata Graphics. Stata Press. (v) The lecture notes on Stata and SAS will be shared later.

This course aims to introduce the application of big data, artificial intelligence, and machine learning techniques in the decision-making processes of businesses. It is designed to help students understand these technologies and gain practical skills. By the end of the course, students are expected to understand the fundamental concepts and methods of artificial intelligence, grasp supervised learning techniques, and learn programming languages such as Stata and SAS to analyze business decision-making strategies practically. Throughout the course, students will develop skills in selecting appropriate datasets, using analytical tools, interpreting results, and reporting findings effectively.

1. The students can analyze the impact of artificial intelligence and machine learning on business processes.
2. The students can apply data analysis and software tools (Stata, SAS) to decision-making processes.
3. The students can make informed choices between different artificial intelligence models.

Week 1: Course Introduction and Overview
Week 2: Big Data and Its Applications in Business
Week 3: Fundamental Concepts and Methods of Artificial Intelligence
Week 4: Supervised Learning Techniques
Week 5: Data Management and Basic Analyses with Stata
Week 6: Data Management and Basic Analyses with Stata
Week 7: Applications of Machine Learning
Week 8: Midterm Exam
Week 9: Regression Models and Decision Trees
Week 10: Nonlinear Models and Neural Networks
Week 11: Model Evaluation and Decision Support Systems
Week 12: Student Presentations and Project Work

Midterm 30%
Final 30%
Participation 10%
Project and Presentations: 30%