Information Package / Course Catalogue
Aı and Data Analytics Applications in Logistics
Course Code: LGT162
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The aim of this course is to provide students with an understanding of the role of artificial intelligence and data analytics in logistics processes; and to equip them with the skills to develop decision support systems, enhance process efficiency, and design digital transformation strategies using these technologies.

Course Content

Fundamentals of artificial intelligence, basic concepts of machine learning, logistics-specific application examples, data processing with tools like ChatGPT, generating Python code, understanding statistical analysis, basic data analysis with SPSS, and data visualization techniques using Excel. The course aims to develop data literacy and AI-supported decision-making in logistics without requiring prior coding knowledge.

Name of Lecturer(s)
Learning Outcomes
1.Apply artificial intelligence and machine learning concepts to logistics-related applications.
2.Interpret and apply basic AI algorithms such as classification, regression, and clustering in relevant scenarios.
3.Use ChatGPT and similar language models to build and implement basic data processing and analysis scenarios.
4.Utilize descriptive statistics and data visualization techniques using tools such as Excel, Google Sheets, or SPSS.
5.Perform and interpret basic analyses such as correlation and regression using AI tools or statistical software.
6.Design AI-supported decision scenarios and develop data-driven solutions for real-world logistics problems.
Recommended or Required Reading
1.Machine Learning by Prof. Necmi Gürsakal
2.Machine Learning with Python by Prof. Ömer Deperlioğlu & Assoc. Prof. Utku Köse
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to AI: Concepts, history, and its relevance to logistics
Week 2 - Theoretical
AI applications in logistics: Forecasting, routing, warehouse automation
Week 3 - Theoretical
Introduction to machine learning: Supervised vs. unsupervised learning
Week 4 - Theoretical
Basic algorithms: Regression, classification, clustering (conceptual overview)
Week 5 - Practice
Data cleaning and text classification with ChatGPT and similar tools
Week 6 - Practice
Generating Python code with ChatGPT: Creating charts and tables
Week 7 - Theoretical
Introduction to modern data tools: Power BI, MonkeyLearn, Orange
Week 8 - Theoretical
What is data? Types of data, mean, median, and standard deviation
Week 9 - Theoretical
Descriptive statistics and data-driven thinking
Week 10 - Practice
Data visualization: Line, bar, and pie charts using Excel/Sheets
Week 11 - Practice
Basic SPSS analysis: Correlation and regression applications
Week 12 - Practice
Interpreting, classifying, and summarizing data with ChatGPT
Week 13 - Practice
Project work: Designing AI-supported logistics decisions
Week 14 - Theoretical
General review: Key concepts, tools, sample analyses and interpretations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%10
Assignment1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142028
Lecture - Practice1415
Assignment1415
Midterm Examination1415
Final Examination1617
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
OÇ-1
5
3
OÇ-2
4
OÇ-3
4
4
4
OÇ-4
4
OÇ-5
3
4
OÇ-6
Adnan Menderes University - Information Package / Course Catalogue
2026