Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTICAL DATA ANALYSIS 200100715411 3 + 0 3.0 7.0

Prerequisites None

Language of Instruction English
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors İlkay Türk Çakır
Assistants
Goals The aim of the lesson is the practical application of statistics to data analysis in physical sciences. It primarily aims to draw quantitative conclusions from experimental data. It is aimed to gain the applicability of statistical distributions, errors, statistical precision value, signal and background calculations, Monte Carlo event productions used in data analysis to elementary particle collision and decays.
Course Content The basic tools of data analysis, concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation, advanced statistical ideas, focusing on interval estimation, characteristic functions, and correcting distributions for the effects of measurement errors (unfolding).
Learning Outcomes 1) Learns the basic concepts of statistics.
2) Gains computational ability in this field by learning accelerator physics, detector physics and probability functions used in data analysis.
3) Learns Monte Carlo Methods, which are frequently used in many fields of physics and other fields.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Fundamental concepts Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
2. Week Examples of Probability functions Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
3. Week The Monte Carlo Method Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
4. Week Statistical Tests -I Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
5. Week Statistical Tests -II Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
6. Week General Concepts of parameter estimation Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
7. Week Midterm Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
8. Week The method of Maximum Likelihood-I Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
9. Week The method of Maximum Likelihood-II Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
10. Week The method of least squares Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
11. Week Statsitical errors, confidence intervals and limits-I Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
12. Week Statsitical errors, confidence intervals and limits-II Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
13. Week Characteristic functions and related examples Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
14. Week Unfolding Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Statistical Methods in Medical Research, 4th Edition, Peter Armitage, Geoffrey

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15444
PY25000
PY35000
PY45000
PY55000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

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 5
Homework 3 5
Presentation (Including Preparation Time) 1 10
Project (Including Preparation and presentation Time) 1 10
Report (Including Preparation and presentation Time) 1 10
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 20
Final Exam 1 5
Time to prepare for Final Exam 1 25
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
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload