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Information
| Code | ZO017 |
| Name | Data Pre-processing in Knowledge Discovery |
| Term | 2024-2025 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ZEYNEL CEBECİ |
Course Goal / Objective
This course aims to teach the topics on data pre-processing techniques and methods.
Course Content
This course includes the topics on data pre-processing techniques and methods for statistical data analysis and data engineering.
Course Precondition
No prerequisites
Resources
Cebeci, Z. (2020). Data Preprocessing With R in Data Science. Nobel Akademik Yayıncılık, Ankara. ISBN 9786254060755
Notes
De Jonge, E., & Van Der Loo, M. (2013). An introduction to data cleaning with R. Heerlen: Statistics Netherlands. URL https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learns evalaution of data quality. |
| LO02 | Learns data integration. |
| LO03 | Learns missing values/outliers detection. |
| LO04 | Learns efficient memory managment. |
| LO05 | Learnd data management. |
| LO06 | Learns how to process and impute the missing values in a data set. |
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. | 3 |
| 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. | 5 |
| 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. | 1 |
| 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 to knowledge disvcovery process | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Measurement scales and data types | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Working with R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Data types and data structures in R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 5 | Arithmetical and logical operations in R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama |
| 6 | Data input and output in R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Data quality assessment | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Preparation for the exam | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 9 | Data integration and selection | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 10 | Data check and cleaning | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | R packages for data check | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Data transformatiton and data reduction | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Analysis of big data 1 | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Analysis of big data 2 | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Introduction to parallel processing | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Ödev |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Ödev |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 152 | ||
| Total Workload / 25 (h) | 6,08 | ||
| ECTS | 6 ECTS | ||