Information Package / Course Catalogue
Statistical Programming
Course Code: CSE214
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The aim of this course is to enable students to use the Python programming language at a level where they can write functions, apply statistical analyses they have learned in the field of statistics, and gain preliminary knowledge about implementing more advanced analyses they will encounter in the future.

Course Content

Python installation, objects in Python, data processing, data visualization, functions, programming in Python, and statistical applications in Python.

Name of Lecturer(s)
Learning Outcomes
1.Explain probability distribution.
2.Understand measures of central tendency.
3.Writing functions in Python
4.Apply discrete and continuous probability distributions
5.Performing statistical analysis in Python
Recommended or Required Reading
1.Walpole, Ronald E.; Myers, Raymond H.; Myers, Sharon L.; Ye, Keying E.; Probability and Statistics for Engineers and Scientists; ISBN-13: 978-0321629111; Edition: 9; 2011.
2.Akdeniz, F. İstatistik ve Olasılık, A.Ü yayınları, 1995.
3.Cevdet Cerit, Olasılık, İstanbul.
4.R yazılımına Giriş, Özlem İlk, Otdü Yayıncılık, 2011.
5.Beginners Guide to R, A. F. Zuur, E. N. Ieno, E.H.W.G. Meesters, Springer, 2009.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Python and Jupyter notebook
Week 2 - Theoretical
Variable types
Week 3 - Theoretical
Descriptive statistics
Week 4 - Theoretical
Graphs
Week 5 - Theoretical
Data wrangling
Week 6 - Theoretical
Basic programming
Week 7 - Theoretical
Introduction to probability
Week 8 - Theoretical
Parameter estimation
Week 9 - Theoretical
Hypothesis testing – I
Week 10 - Theoretical
Hypothesis testing – II
Week 11 - Theoretical
Categorical data analysis
Week 12 - Theoretical
Comparing two means
Week 13 - Theoretical
Comparing more means
Week 14 - Theoretical
Linear regression
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures8%5
Assignment1%10
Midterm Examination1%25
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Lecture - Practice141242
Assignment110212
Midterm Examination115217
Final Examination118220
TOTAL WORKLOAD (hours)147
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
OÇ-1
4
3
1
1
2
3
2
1
1
2
OÇ-2
4
3
1
1
4
3
2
1
1
2
OÇ-3
1
2
3
5
2
2
2
3
1
2
1
OÇ-4
4
5
3
4
4
1
2
3
1
2
1
OÇ-5
5
4
2
3
5
5
4
1
2
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026