Master Program in Information Technologies

Python Programming(IT 513)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
IT 513 Python Programming 1 4 0 0 3 8
Prerequisites
Admission Requirements
Language of Instruction English
Course Type Compulsory
Course Level Masters Degree
Course Instructor(s) Pınar ULUER puluer@gsu.edu.tr (Email)
Assistant
Objective In this course, students are introduced to the fundamentals of algorithmic thinking and the core concepts of programming through hands-on applications using the Python programming language. Building on these foundations, the course aims to equip students with the knowledge and experience to define data-driven problems, develop and propose solution strategies, implement these solutions in Python, and evaluate them based on various performance criteria.
Content This course aims to equip students with general programming skills and algorithmic thinking through the fundamental concepts of the Python programming language. In this context, the course begins with basic data structures and control flow in Python and covers topics such as the numpy and pandas libraries commonly used in data analysis, the matplotlib and seaborn libraries for data visualization, statistical data analysis and data preprocessing, and some examples from machine learning methods. Through hands-on applications carried out as part of the course, students gain experience in analyzing and processing real-world data they encounter for the first time, as well as in building models to extract meaningful insights from data. In addition, they develop the ability to write modular Python code.
Course Learning Outcomes Upon successful completion of this course, students will be able to:
1. Apply fundamental concepts of algorithms and programming using the Python programming language.
2. Perform data analysis and preprocessing tasks using Python.
3. Analyze a given problem, develop an appropriate model, and implement it using Python.
4. Evaluate and interpret the performance of the developed model based on various performance metrics.
Teaching and Learning Methods Theory and practice, presentation, discussion, question-answer
References Learning Python, 6th Edition by Mark Lutz, February 2025, O'Reilly Media, Inc. ISBN: 9781098171308
Python Data Science Handbook, 2nd Edition by Jake VanderPlas, December 2022, O'Reilly Media, Inc. ISBN: 9781098121228
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 Introduction to Algorithms and Programming Languages
2 Introduction to Python Programming
3 Python Libraries I: Numpy & Pandas
4 Descriptive Statistics and Preprocessing with Pandas
5 Python Libraries II: Matplotlib & Seaborn
6 Data Visualization
7 Python Libraries III: Sklearn
8 Machine Learning Algorithms
9 Practice Session I: Problem Definition, Data Preprocessing
10 Practice Session II: Modeling and Performance Evaluation
11 Student Presentations
Practice Topics
Week Weekly Contents
Assessment System
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 1 40
Contribution of final exam to overall grade 1 60
Toplam 2 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 1 40
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Make-up 0 0
Toplam 1 40
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X
9 X
10 X
11 X
ECTS
Activities Number Period Total Workload
Class Hours 10 4 40
Working Hours out of Class 10 4 40
Assignments 0 0 0
Presentation 0 0 0
Midterm Examinations (including preparation) 1 55 55
Project 0 0 0
Laboratory 0 0 0
Other Applications 0 0 0
Final Examinations (including preparation) 1 55 55
Quiz 0 0 0
Term Paper/ Project 0 0 0
Portfolio Study 0 0 0
Reports 0 0 0
Learning Diary 0 0 0
Thesis/ Project 0 0 0
Seminar 0 0 0
Other 0 0 0
Make-up 0 0 0
Yıl Sonu 0 0 0
Hazırlık Yıl Sonu 0 0 0
Hazırlık Bütünleme 0 0 0
Total Workload 190
Total Workload / 25 7.60
Credits ECTS 8
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