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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| BUSINESS ADMINISTRATION (PhD) | |
| Code | MG5808 |
| Name | |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 8 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
Teaching the application processes of what can be done within the scope of machine learning and data mining with WEKA and R languages. Teaching what can be done on big data.
Course Content
Machine learning, data mining, artificial intelligence concepts and application with WEKA and R languages. Analyzes that can be applied on big data.
Course Precondition
None
Resources
Data Mining. Parteek Bhatia.
Notes
There is no additonal text book in this course.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the concepts of machine learning and artificial intelligence. |
| LO02 | Lists the operations that can be done within the scope of data mining. |
| LO03 | Explains the classification methods required to construct a decision tree. |
| LO04 | Recognizes the WEKA program, which is an open source software, and uses it for data mining. |
| LO05 | It recognizes the R language, which is an open source software, and uses codes for data mining. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains the basic theoretical models for business field | 1 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Lists and identifies the theories that will contribute to the development of scientific methods and tools used in business | 1 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has an understanding of the legal and ethical issues faced by the Business profession | 1 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Explains how to interpret the findings as a result of models used in business methods. | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Creates sufficient knowledge to find a solution to the problems met by business | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Contributes to business by following the basic steps of the methods used in business | 2 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the application of business management methods. | 1 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Encourages taking responsibility, claiming the lead and working effectively in a team and / or individually. | 2 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Keeps track of the latest developments in the field as a recognition of the need for lifelong learning and constant renewal | 1 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Utilizes scientific sources in the field, collect the data, synthesizes the obtained information and presents the outcomes effectively | 4 |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Has a good command of Turkish, as well as at least one another foreign language in accordance with the requirements of academic and work life | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Develops and implements new research methods that will contribute to the development of the business field | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Develops new guidelines for the business managers’ decision making processes by researching on sub-disciplines of the business field. | 4 |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Forms the basis for the decision-making process by researching on the science of business field | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Machine learning | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Artificial intelligence | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Introduction to data mining | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Getting started with Weka | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 5 | Getting started with R | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 6 | Data preprocessing | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Classification | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 8 | Midterm Exam | Studying for exam | Ölçme Yöntemleri: Ödev |
| 9 | Classification applications with Weka | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Classification applications with R language | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Cluster analysis | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 12 | Clustering applications with Weka and R | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Association rule | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Web Mining and Search Engines | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Data warehouse and big data | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 16 | Final Exam 1 | Preparation for Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Final Exam 2 | Preparation for Exam | Ölçme Yöntemleri: Yazılı Sınav |
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 | 8 | 112 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 4 | 8 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 24 | 24 |
| Total Workload (Hour) | 212 | ||
| Total Workload / 25 (h) | 8,48 | ||
| ECTS | 8 ECTS | ||