Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
FUNDAMENTALS OF PROGRAMMING II SGM102 2. Semester 3 + 2 4.0 5.0

Prerequisites None

Language of Instruction Turkish
Course Level Associate's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals This course aims to develop the student's programming fundamentals at an advanced level. It is aimed at providing students with the ability to perform advanced Python operations such as database management, data analysis, web development, interface design, and object-oriented programming. While the student is taught advanced knowledge of Python programming, applications are made about programming logic.
Course Content Advanced Python, Web Development, Web Scraping, Data Analysis
Learning Outcomes 1) Gain the ability to create or edit a database with Python Analyzing data with various libraries and visualizing data with Python.
2) Gain the ability to develop a website. Gaining the ability to retrieve data from the internet or automatically enter data from the internet. Gaining the ability to design interfaces with Python.
3) Gain the ability to develop advanced programming algorithms by learning advanced data structures, object-oriented programming, advanced modules, and embedded functions.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Basic Concepts Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Problem Based Learning 		Seminar
2. Week Advanced Data Structures and Embedded Functions Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Problem Based Learning 		Seminar
3. Week Advanced Functions, Decorators, Iterators, and Generators Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Project Based Learning; Problem Based Learning 		Seminar
4. Week Advanced Modules and Web Scraping Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Problem Based Learning 		Seminar
5. Week Object-oriented programming Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Project Based Learning; Problem Based Learning 		Seminar
6. Week Interacting with Web Pages with Selenium Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Problem Based Learning 		Seminar
7. Week SQL Database Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Project Based Learning; Problem Based Learning 		Seminar
8. Week Midterm exam Question Answer; Problem Solving

Problem Based Learning 		Seminar
9. Week Interface Development with PyQt5 Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Project Based Learning; Problem Based Learning 		Homework Seminar
10. Week Web development with Django Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming
Problem Based Learning 		Seminar
11. Week Web development with Django Lecture; Question Answer; Problem Solving; Discussion; Case Study; Role Play
Brainstorming
Project Based Learning; Problem Based Learning 		Homework Seminar
12. Week Data Analysis with Numpy Lecture; Question Answer; Problem Solving; Discussion; Case Study; Role Play
Brainstorming
Problem Based Learning 		Seminar
13. Week Data Analysis with Pandas Lecture; Question Answer; Problem Solving; Discussion; Case Study; Role Play
Brainstorming
Problem Based Learning 		Seminar
14. Week Data Visualization with Matplotlib Lecture; Question Answer; Problem Solving; Discussion; Case Study; Role Play
Brainstorming
Project Based Learning; Problem Based Learning 		Homework Seminar

Sources Used in This Course
Recommended Sources
David Beasley & Brian K. Jones, Python Cookbook: Recipes for Mastering Python 3, 3rd Edition, O'Reilly Media, Inc., 2013.
Eric Matthes, Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 3rd Edition, No Starch Press, 2023.
Luciano Ramalho, Fluent Python, 2nd Edition, O'Reilly Media, Inc., 2022.
Wes McKinney, Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15000
PY25555
PY35000
PY45555
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 3
Homework 3 3
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 14 2
Quiz 3 1
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 8
Final Exam 1 2
Time to prepare for Final Exam 1 14
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