ZO017 Data Pre-processing in Knowledge Discovery

6 ECTS - 4-0 Duration (T+A)- . Semester- 4 National Credit

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

Update Time: 13.05.2024 01:45