Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3ENM207İleri Seviye Programlama3+2+04501.11.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Industrial Engineering
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course Advanced instruction in computer programming using the Python programming language, and the development of the ability to apply the knowledge gained through practical projects.
Course Content What is a database? Types of databases, relational database design, server and IDE setup, creating tables within an IDE, connecting to a database using Python, performing CRUD operations (adding, deleting, updating, and reading data) via Python, filtering and sorting records in a database using Python, aggregate functions, working with multiple tables, SQLite, NoSQL, JSON, HTTP/API, introduction to machine learning, and the NumPy and Pandas libraries.
Course Methods and Techniques insite
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Mehmet AYAS
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 1-https://docs.python.org/3/tutorial/index.html (The Python Tutorial) 2-Scikit-learn: Machine Learning in Python 3-Python ile Sıfırdan İleri Seviye Programlama, Sadık Turan, Udemy 4-Python A-Z™: Veri Bilimi ve Machine Learning, Vahit Keskin, Udemy Python ile Makine Öğrenmesi, Prof.Dr.Sadi Enver Şeker, Udemy
Course Notes 1-https://docs.python.org/3/tutorial/index.html (The Python Tutorial)
2-Scikit-learn: Machine Learning in Python
3-Python ile Sıfırdan İleri Seviye Programlama, Sadık Turan, Udemy
4-Python A-Z™: Veri Bilimi ve Machine Learning, Vahit Keskin, Udemy
Python ile Makine Öğrenmesi, Prof.Dr.Sadi Enver Şeker, Udemy
Documents .
Assignments .
Exams .

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 15 3 45
Hours for off-the-c.r.stud 15 5 75
Practice 15 2 30
Total Work Load   Number of ECTS Credits 5 150

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 1-İlişkisel veritabanı mantığını kavrama,
2 2-Veritabanı için sunucu ve entegre geliştirme ortamı kurulumu ve IDE üzerinde tablo oluşturma mantığını öğrenme,
3 3-Yazılım üzerinden veritabanına bağlanarak veri ekleme bilgisi kazanma,
4 4-Yazılım üzerinden veritabanına bağlanarak veri okuma, kayıt filtreleme ve sıralama beceresi kazanma,
5 5-Yazılım üzerinden veritabanına bağlanarak aggregate fonksiyonlarının mantığını kavrama ve veri güncellemeyi öğrenme,
6 6-Birden fazla tablo ile ilişkisel olarak çalışmayı ve kayıt silmeyi öğrenme,
7 7-Sunucusuz veritabanı, JSON mantığını kavrama,
8 8-HTTP istemci ve sunucu arasındaki veri alışverişi protokolünü ve yöntemlerini kavrama ve API’le ile iletişim kurmayı öğrenme,
9 9-Makine öğrenmesi mantığını kavrama,
10 Makine öğrenme algoritmalarını çeşitli kütüphaneleri kullanarak kodlama beceresi kazanma,
11 Python ile proje yazma becerisi kazanma,

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 What is a database? Types of databases, relational database design, database server
2 Server and IDE installation, creating tables within the IDE
3 Connecting to a database using Python, adding data to a database via Python
4 Reading data from a database using Python
5 Filtering and sorting records in a database using Python
6 Aggregate functions, record update
7 Deleting records in a database using Python, working with multiple tables
8 Ara Sınav Haftası
9 Sqlite, NoSql , Json
10 Http - Api
11 Introduction to Machine Learning
12 Numpy Kütüphanesi
13 Pandas Kütüphanesi
14 Project
15 Project

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24
All 1 1 1 5 1 1 1 1 1 1
C1 5 1 1 1 1 1
C2 5 1 1 1 1 1
C3 5 1 1 1 1 1
C4 5 1 1 1 1 1
C5 5 1 1 1 1 1
C6 5 1 1 1 1 1
C7 5 1 1 1 1 1
C8 5 1 1 1 1 1
C9 5 1 1 1 1 1
C10 5 1 1 1 1 1
C11 5 3 5 1 3 1 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant