Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTICAL METHODS AND DATA ANALYSIS 805101725071 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors DENİZER YILDIRIM
Assistants
Goals This course aims to instruct students in cultivating a data-driven mindset, improving analytical problem-solving abilities, and employing statistical methods with confidence. By doing so, it equips them with the essential tools and skills needed to tackle challenges across various domains.
Course Content I. Understanding and Interpreting Data: Students will acquire the ability to comprehend and interpret data, allowing them to extract fundamental properties and relationships from data sets. II. Statistical Thinking: Students will learn the principles of statistical thinking and acquire the skills needed to address real-world problems using statistical approaches. They will develop the capacity to make data-driven decisions. III. Basic Statistical Methods: Students will become proficient in fundamental statistical methods and gain experience in topics such as measures of central tendency, measures of dispersion, probability theory, hypothesis testing, and regression analysis. IV. Data Collection and Experimental Design: Students will grasp the data collection process and cultivate the skills required to formulate robust experimental designs. V. Statistical Software and Tools: Students will enhance their proficiency in using statistical software tools like R, Python, and SPSS, enabling them to analyze data effectively. VI. Practical Applications: Students will refine their data analysis skills through hands-on projects and practical examples. VII. Statistical Critical Thinking: Students will develop expertise in critically evaluating statistical results. VIII. Collaboration and Communication: Students will improve their ability to elucidate data analysis outcomes and communicate them effectively.
Learning Outcomes

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Understanding and Interpreting Data Lecture; Question Answer; Problem Solving

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
2. Week Understanding and Interpreting Data Lecture; Question Answer; Problem Solving; Discussion

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
3. Week Statistical Thinking Lecture; Question Answer; Problem Solving; Discussion

 Homework Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
4. Week Statistical Thinking Lecture; Question Answer; Problem Solving; Discussion

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
5. Week Basic Statistical Methods Lecture; Question Answer; Problem Solving; Discussion

 Homework Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
6. Week Basic Statistical Methods Lecture; Question Answer; Problem Solving

 Homework
7. Week Basic Statistical Methods Lecture; Question Answer; Problem Solving; Discussion

 Homework
8. Week Data Collection and Experimental Design Lecture; Question Answer; Problem Solving; Discussion

 Homework Presentation (Including Preparation Time)
Midterm Discussion

 Homework Presentation (Including Preparation Time)
9. Week Data Collection and Experimental Design Lecture; Question Answer; Problem Solving

 Homework
10. Week Statistical Software and Tools Lecture; Question Answer; Problem Solving; Discussion

 Homework Presentation (Including Preparation Time)
11. Week Statistical Software and Tools Lecture; Question Answer; Problem Solving; Discussion

 Homework Presentation (Including Preparation Time)
12. Week Practical Implementation Lecture; Question Answer; Problem Solving; Discussion

 Homework Report (Including Preparation and presentation Time)
13. Week Collaboration and Communication Lecture; Question Answer; Problem Solving; Discussion

 Homework Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
14. Week Final

Project Based Learning; Problem Based Learning 		Homework Project (Including Preparation and presentation Time)

Sources Used in This Course
Recommended Sources
Ott, R. L., & Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Cengage Learning.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 4
Homework 2 5
Midterm Exam 1 10
Time to prepare for Midterm Exam 1 20
Time to prepare for Final Exam 1 20
1 2
1 2
7 10
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload