Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Science
  • Department of Statistics
  • Course Structure Diagram with Credits
  • Modern Data Systems
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title MTH4506 - Modern Data Systems
Course Type Area Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 5.0
Course Instructor(s) Ergun BİÇİCİ
Mode of Delivery The mode of delivery of this course is online.
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course.
Required or Recommended Resources The instructor will provide a course pack(bulk pack) with all suggested readings
Recommended Reading List
Assessment methods and criteria Two midterms and a final exam
Work Placement
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Introduction to Database Systems
Week - 2 Data Modeling and Data Models
Week - 3 Relational Data Models, Entity-Relationship Modeling, and Distributed Data Management
Week - 4 Advanced Data Modeling and Data Lifecycle
Week - 5 Introduction to Big Data and Normalization
Week - 6 Database Design and Development Strategies
Week - 7 Data warehouses, Online Analytical Processing (OLAP)
Week - 8 Introduction to Structured Query Language (SQL)
Week - 9 Data Mining, Big Data Analytics, NoSQL
Week - 10 Data Visualization Fundamentals and Big Data Visualization Techniques
Week - 11 Introduction to Machine Learning and its Methods
Week - 12 Text Processing and Applications
Week - 13 Introduction to Search and Recommendation Systems
Week - 14 Introduction to and Prompting Large Language Models (LLM)

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Observation
  • Experiment
  • Case Study
  • Competences
  • Productive
  • Questoning
  • Entrepreneur
  • Creative
  • Follow ethical and moral rules
  • Effective use of Turkish
  • Use time effectively
  • Eleştirel düşünebilme
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • To work autonomously
  • Organization and planning
  • Elementary computing skills
  • Decision making
  • To work in interdisciplinary projects

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 25
Project 1 35
Final Exam 1 40
Toplam (%) 100
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