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| YZT1005 | Introduction to Data Science | 3+2+0 | ECTS:6 | | Year / Semester | Fall Semester | | Level of Course | Short Cycle | | Status | Compulsory | | Department | Computer Technology | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures and 2 hours of practicals per week | | Lecturer | Öğr. Gör. Didem ÇAKIR | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | |
It aims to provide introductory-level knowledge in data science, specializing in the fields of machine learning, artificial intelligence, and big data. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | They acquire basic knowledge of data science. | 1 | 1, | | LO - 2 : | They acquire basic knowledge of statistics. | 2,3 | 1, | | LO - 3 : | They can perform data analysis. | 3,5 | 1, | | LO - 4 : | They can implement projects in the field of data science. | 6,7 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| In the field of data science, students acquire fundamental data literacy knowledge, perform data analysis, and develop software skills related to data science. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | What is Data Science? | | | Week 2 | History of Data Science and Its Relationship with Other Sciences | | | Week 3 | What is Data? | | | Week 4 | Types of Data | | | Week 5 | Data Literacy | | | Week 6 | Data Structures | | | Week 7 | Data Structures (repeated topic) | | | Week 8 | Data Collection Methods | | | Week 9 | Midterm Exam | | | Week 10 | Introduction to Data Analytics | | | Week 11 | Example Applications of Data Analysis | | | Week 12 | Types of Graphs and Data Visualization | | | Week 13 | Types of Graphs and Data Visualization (repeated topic) | | | Week 14 | Data Visualization Applications (Python, Matlab, etc.) | | | Week 15 | Data Visualization Applications (Python, Matlab, etc.) | | | Week 16 | For this course, the Midterm Exam is scheduled for a date between the 9th and 16th weeks. From the date the exam is held, the topics are postponed by one week. | | | |
| 1 | VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. Sebastopol, CA: O'Reilly Media.
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Güven, F. (2021). Veri Bilimine Giriş ve Python ile Uygulamalar. Ankara: Akademik Yayınları. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 50 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 5 | 14 | 70 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 1 | 14 | 14 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 3 | 14 | 42 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 170 |
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