| Course Objectives: |
The aim of this course is to provide students with an understanding of the fundamental concepts of data science and to enable them to apply basic techniques of data cleaning, exploratory analysis, visualization, and simple modeling using the Python programming language. The course is designed to equip software engineering and industrial engineering students with the ability to work with data, develop analytical thinking skills, and gain familiarity with widely used data science tools. |
| Course Content: |
This course introduces the fundamental concepts and applications of data science. Students will gain knowledge of data types and structures, data sources, data cleaning and preprocessing, exploratory data analysis, data visualization, basic probability and statistics, regression and classification methods, model evaluation metrics, and clustering techniques. Throughout the course, students will work with small-scale applications using Python and popular data science libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn. By the end of the semester, students will complete a group project analyzing a real dataset. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Explain the fundamental concepts, processes, and application areas of data science.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Perform data cleaning, analysis, and visualization on small-scale datasets using Python.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) Apply basic statistical methods as well as simple regression, classification, and clustering techniques.
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2) Interpret findings from real datasets and present them in written reports and oral presentations.
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
Explain the fundamental concepts, historical development, and theoretical framework of graphic design. |
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| 2) |
Define typography, color theory, and composition principles in visual communication design. |
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| 3) |
Evaluate the social, cultural, and ethical aspects of graphic design to develop an interdisciplinary perspective. |
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| 4) |
Develop original and innovative design solutions using creative problem-solving methods. |
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| 5) |
Apply visual hierarchy, perception psychology, and user experience (UX) principles to design for international markets. |
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| 6) |
Effectively use digital tools and design software to produce professional graphic design work. |
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| 7) |
Take responsibility in international graphic design projects individually or within a team to develop creative solutions. |
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| 8) |
Manage graphic design projects and plan processes while applying a professional work discipline. |
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| 9) |
Continuously improve by following global innovations, technologies, and methodologies in graphic design. |
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| 10) |
Adopt intercultural design principles to create visual solutions for global audiences. |
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| 11) |
Develop design solutions that are culturally sensitive, ethically appropriate, and sustainable. |
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| 12) |
Work independently or participate in teamwork within graphic design processes. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
13 |
3 |
39 |
| Laboratory |
0 |
0 |
0 |
| Application |
0 |
0 |
0 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
1 |
65 |
65 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
1 |
25 |
25 |
| Homework Assignments |
1 |
10 |
10 |
| Total Workload of Teaching & Learning Activities |
- |
- |
139 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
0 |
0 |
0 |
| Midterms |
1 |
3 |
3 |
| Semester Final Exam |
1 |
3 |
3 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
6 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
145 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |