Chemistry (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

COE103

Course Name:

Computational Thinking

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Compulsory Courses

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. MUHAMMED DAVUD

Course Lecturer(s):

Asst. Prof. Muhammed Davud

Course Assistants:

Course Objective and Content

Course Objectives:

This course offers a comprehensive exploration of computer science fundamentals. It covers programming languages, algorithm design, efficiency analysis, and various algorithm paradigms. Students will also apply computational thinking using Python.

Course Content:

This course aims to understand the fundamentals of computer science, including the significance of programming languages and the historical context of computing. It will focus on the design and analysis of algorithms with an emphasis on efficient problem-solving strategies. Students will evaluate the efficiency of algorithms considering time and space complexity and apply this knowledge to real-world problem-solving. Various algorithmic paradigms, such as Brute Force, Decrease-and-Conquer, Divide-and-Conquer, and Transform-and-Conquer, will be applied to address diverse problem domains. Additionally, students will apply computational thinking in practical contexts using Python, enhancing their ability to solve real-world problems effectively.

Learning Outcomes

The students who have succeeded in this course;
1) Understand the fundamentals of computer science, including the significance of programming languages and the historical context of computing.
2) Design and analysis algorithms, with a focus on efficient problem-solving strategies.
3) Evaluate the efficiency of algorithms, considering time and space complexity, and apply this knowledge to real-world problem-solving.
4) Apply different algorithmic paradigms, including Brute Force, Decrease-and-Conquer, Divide-and-Conquer, and Transform-and-Conquer, to address diverse problem domains.
5) Apply computational thinking in practical contexts using Python.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction
2)	Computer and Programming Languages
3)	Algorithm Design and Flow Charts
4)	Algorithm Design and Flow Charts
5)	Analysis of Algorithm Efficiency
6)	Brute Force and Exhaustive Search
7)	Decrease-and-Conquer
8)	Midterm
9)	Divide-and-Conquer
10)	Transform-and-Conquer
11)	Space and Time Tradeoffs
12)	Iterative Improvement Algorithms
13)	Introduction to Python
14)	Applied Computational Thinking Using Python

Sources

Course Notes / Textbooks:	1- Introduction to the Design & Analysis of Algorithms - 3rd edition, by Anany Levitin. 2- Computational Thinking, A beginner’s guide to problem-solving and programming, Karl Beecher, 2017.
References:	Lecture Notes.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Knows the basic concepts related to the theory and applications of chemistry, uses theoretical and applied knowledge, can select, develop and design methods.
2) Makes experimental planning and application for analysis, synthesis, separation and purification methods, provide solutions to the problems encountered and interpret the results.		2	2	2
3) Expresses the basic principles of sample preparation techniques and instrumental analysis methods used in qualitative and quantitative analysis of items, discusses their application areas.
4) Has knowledge about the sources, production, industrial applications and technologies of chemical substances.
5) Makes structural analyzes of chemical substances and interprets the results.
6) Work individually and in multidisciplinary groups, take responsibility, plan their tasks and use time effectively.
7) Follows the information in the field and communicates with colleagues by using English at a professional level.
8) Uses information and communication technologies along with computer software at the level required by the field.	3		3		3
9) Follows the national and international chemistry literature, transfers the knowledge gained orally or in writing.
10) Determines self-learning needs, manages/directs his/her learning.
11) Takes responsibility and adheres to the ethical values required by these responsibilities.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Knows the basic concepts related to the theory and applications of chemistry, uses theoretical and applied knowledge, can select, develop and design methods.
2)	Makes experimental planning and application for analysis, synthesis, separation and purification methods, provide solutions to the problems encountered and interpret the results.	2
3)	Expresses the basic principles of sample preparation techniques and instrumental analysis methods used in qualitative and quantitative analysis of items, discusses their application areas.
4)	Has knowledge about the sources, production, industrial applications and technologies of chemical substances.
5)	Makes structural analyzes of chemical substances and interprets the results.
6)	Work individually and in multidisciplinary groups, take responsibility, plan their tasks and use time effectively.
7)	Follows the information in the field and communicates with colleagues by using English at a professional level.
8)	Uses information and communication technologies along with computer software at the level required by the field.	3
9)	Follows the national and international chemistry literature, transfers the knowledge gained orally or in writing.
10)	Determines self-learning needs, manages/directs his/her learning.
11)	Takes responsibility and adheres to the ethical values required by these responsibilities.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	13	39
Application	14	14
Study Hours Out of Class	14	28
Midterms	2	17
Final	1	15
Total Workload		113