Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | EĞİTİMDE YAPAY ZEKA TEKNOLOJİLERİ(TEZLİ YÜKSEK LİSANS ÖNERİLEN) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | ŞEYHMUS AYDOĞDU (saydogdu@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to teach students the basic concepts of programming and data science, and to provide students with the competence to make data-driven decisions using data analysis and machine learning techniques. Students will develop data collection, cleaning, analysis and visualization skills using popular programming languages such as Python or R. In addition, they will learn to build machine learning models by applying supervised and unsupervised learning methods and will be able to use these models in data analysis processes. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | By understanding the core concepts and processes of data science, they can effectively apply data collection, cleaning, analysis, and visualization methods. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-9 Integrates different disciplines with artificial intelligence technologies in education. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| LO-2 | They can write, run, and evaluate data manipulation, data analysis, and machine learning algorithms using programming languages such as Python or R. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to artificial intelligence technologies in teaching profession and education and existing resources effectively. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| LO-3 | Using supervised and unsupervised learning algorithms, they can perform operations such as classification, regression and clustering on data and analyze the results. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-9 Integrates different disciplines with artificial intelligence technologies in education. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction and Basic Concepts, Python or R Programming Fundamentals, Data Structures and Collections, Data Reading, Writing and File Operations, Data Manipulation with NumPy and Pandas Libraries, Data Visualization Fundamentals, Statistical Analysis Fundamentals, Data Cleaning and Preparation, Introduction to Machine Learning | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction and Basic Concepts | Lecture, Question and Answer, Discussion |
| 2 | Python or R Programming Basics | Lecture, Question and Answer, Discussion |
| 3 | Data Structures and Collections | Lecture, Question and Answer, Discussion, Individual Study Method |
| 4 | Data Reading, Writing and File Operations | Lecture, Question and Answer, Discussion, Individual Study Method |
| 5 | Data Manipulation with NumPy and Pandas Libraries | Lecture, Question and Answer, Discussion, Individual Study Method |
| 6 | Data Visualization Basics | Lecture, Question and Answer, Discussion, Individual Study Method |
| 7 | Statistical Analysis Fundamentals | Lecture, Question and Answer, Discussion, Individual Study Method |
| 8 | mid-term exam | |
| 9 | Data Cleaning and Preparation | Lecture, Question and Answer, Discussion, Individual Study Method |
| 10 | Introduction to Machine Learning | Lecture, Question and Answer, Discussion, Individual Study Method |
| 11 | Supervised Learning: Classification Methods | Lecture, Question and Answer, Discussion, Individual Study Method |
| 12 | Supervised Learning: Regression Methods | Lecture, Question and Answer, Discussion, Individual Study Method |
| 13 | Unsupervised Learning: Clustering Methods | Lecture, Question and Answer, Discussion, Individual Study Method |
| 14 | Model Evaluation and Hyperparameter Settings | Lecture, Question and Answer, Discussion, Individual Study Method |
| 15 | Applied Project Work | Lecture, Question and Answer, Discussion, Individual Study Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Aydoğdu, Ş. (2020). Algoritma ve Programlama. Ankara: Pegem Akademi. | |
| 2 | Kelleher, J. D., Tierney, B. (2020). Veri Bilimi. | |
| Required Course instruments and materials | ||
| Textbooks, laptop | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | 16 | 1 | 30 |
| 9.Project | |||
| final exam | 16 | 1 | 30 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 3 | 14 | 42 |
| b) Search in internet/Library | 3 | 14 | 42 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 2 | 14 | 28 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 3 | 7 | 21 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 0 | ||
| final exam | 3 | 1 | 3 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||