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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ZOOTECHNICS (MASTER) (WITH THESIS) | |
| Code | ZO696 |
| Name | Data Preprocessing |
| Term | 2026-2027 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Melis ÇELİK GÜNEY |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to teach the data preprocessing methods required to make raw data suitable for analysis. It is intended for students to gain the ability to identify missing, erroneous, inconsistent, and outlier values in datasets and to handle them using appropriate techniques. Additionally, the course aims to enable them to make data suitable for statistical analysis and modeling by applying processes such as data transformation, scaling, encoding, and data reduction.
Course Content
Fundamental concepts of data preprocessing, data types and data structures, missing data analysis, outlier analysis, transformation, data cleaning, data reduction, data merging, noise reduction, ethics and data privacy in data preprocessing
Course Precondition
There are no prerequisites for this course.
Resources
Course notes prepared by the instructor; Cebeci, Zeynel, 2020. Data Preprocessing with R in Data Science. Nobel Academic Publishing.
Notes
Course textbooks = Data preprocessing
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the importance of data preprocessing in statistical analysis and data science. |
| LO02 | Recognizes different data types and selects appropriate preprocessing methods. |
| LO03 | Detects missing data in datasets and handles it using appropriate methods (such as deletion, imputation, etc.). |
| LO04 | Identifies outliers and applies strategies to handle them. |
| LO05 | Applies data cleaning processes. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | After undergraduate education, increases knowledge in one of the fields of animal breeding and breeding, feeds and animal nutrition, biometrics and genetics. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Understands the interaction between different disciplines | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Gains the ability to develop strategic approaches and produce regional, national or international solutions for the field of animal science | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Zootekni bilimindeki verileri kullanarak bilimsel yöntemlerle bilgiyi geliştirebilme, bilimsel, toplumsal ve etik sorumluluk bilinci ile bu bilgileri kullanabilme becerisini kazanır | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Gains the ability to use and develop information technologies with computer software and hardware knowledge required by the field of animal science. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Gains the ability to convey their own studies or current developments in the field of animal science to groups in the field or other fields of science, verbally and visually. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Gains the ability to evaluate the quality processes of animal products | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Gains the ability to keep animal production dynamic in accordance with changing economic and social conditions. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Gains the ability to follow national and international current issues, to follow developments in lifelong learning, science and technology, to constantly renew themselves and to transfer innovations to animal production. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Absorbs the relationship between animal products and human health and community welfare |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction and basic concepts | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Data types and data structures | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 3 | Missing data analysis 1 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 4 | Missing data analysis 2 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 5 | Outlier analysis 1 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 6 | Outlier analysis 2 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 7 | Transformation | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Internet research on the topic will be recommended by the instructor. | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 9 | Data cleaning | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 10 | Data reduction | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 11 | Data integration and merging | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 12 | Noise reduction | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 13 | Ethics and data privacy in data preprocessing | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 14 | Applied data preprocessing 1 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 15 | Applied data preprocessing 2 | Internet research on the topic will be recommended by the instructor. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 16 | Term Exams | Internet research on the topic will be recommended by the instructor. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Internet research on the topic will be recommended by the instructor. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 30 | 60 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 2 | 2 |
| Total Workload (Hour) | 148 | ||
| Total Workload / 25 (h) | 5,92 | ||
| ECTS | 6 ECTS | ||