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Information
| Code | BBP326 |
| Name | Data Mining Applications in Horticulture |
| Term | 2024-2025 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ZEYNEL CEBECİ |
| Course Instructor |
1 2 |
Course Goal / Objective
Learning the methods and techniques for data mining and artificial neural networks in horticulture, applications and analysis with the relevant methods
Course Content
Data Mining applications on research and production data in horticulture
Course Precondition
To have taken the following courses before: Introductory Statistics Introduction to Information Technologies
Resources
Cebeci, Z. (2020). Data Preprocessing with R in Data Science. Nobel Akademik Yayıncılık. ISBN 9786254060755
Notes
Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition, Cambridge University Press, March 2020. ISBN: 978-1108473989. https://dataminingbook.info/book_html/
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | The terms data science and data mining recognize, organize and process data and data sources. |
| LO02 | Gain working knowledge in the R statistical calculation and visualization environment. |
| LO03 | Recognizes the importance of data preprocessing techniques and data visualization. |
| LO04 | Compares managed and unmanaged learning methods. |
| LO05 | Understands and applies artificial neural networks and deep learning. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Adequate knowledge on subjects specific to the discipline of Mathematics, Science and Agricultural Engineering (Horticulture), ability to use theoretical and applied knowledge in these fields in complex engineering problems | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | The ability to identify and solve problems related to the cultivation, breeding and product preservation of fruit, vegetables, vineyards and ornamental plants in horticulture, the ability to choose and apply appropriate analysis and modeling methods for this purpose. | 1 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | The ability to design in a way that meets the necessary conditions for the cultivation of fruit, vegetables, vineyards and ornamental plants in the open and greenhouse in horticulture and the ability to apply modern design methods for this purpose. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Ability to select and use modern tools necessary for the analysis and solution of complex problems encountered in horticulture practices, ability to use information technologies effectively | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Ability to design and conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research issues in the field of Horticulture | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Breeding of Horticultural Plants, developing new varieties, making selection, protecting genetic resources, producing propagation materials (seeds, seedlings, saplings) of developed varieties, ability to work in individual and multi-disciplinary teams | |
| PLO07 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to write effective reports in the field of Horticulture, to understand written reports, to prepare design and production reports, to make effective presentations, to take and give clear and understandable instructions | 1 |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | Awareness of the necessity of lifelong learning, the ability to access information in the field of Horticulture, to follow the developments in science and technology and to constantly renew oneself | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Behaving in accordance with ethical principles, professional and ethical responsibility in the field of Horticulture, and knowledge of standards used in engineering practices | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Information about applications in business life, such as project management, risk management and change management in the field of Horticulture, awareness of entrepreneurship, innovation, information about sustainable development | |
| PLO11 | Yetkinlikler - Alana Özgü Yetkinlik | Knowledge of the effects of horticultural practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of Horticulture, awareness of the legal consequences of horticultural solutions |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data science and data mining | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Agricultural data and data types | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Statistical computing and visualization with R | Studying lecture notes, Install and run with R software | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Data preprocessing | Studying lecture notes, Build the data sets in R | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Data visualization | Studying lecture notes, Drawing histograms, scatterplot, barplot, pie graphs in R | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Statistical data mining methods 1 | Studying lecture notes, Reading the tutorials on the Web | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Statistical data mining methods 2 | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Studying lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Statistical data mining methods 3 | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Undsupervised learning techniques | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Supervised learning techniques | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Artificial Neural Networks 1 | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Artificial Neural Networks 2 | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Deep Learning applications 1 | Studying lecture notes | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Deep Learning applications 2 | Studying lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Studying lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Studying lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||