To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms.
Course Content
This course contains; Introduction,Data warehouse and OLAP,Data Preparation for data analysis , data cleaning noise reduction.,Data mining task analysis problem description,Clustering and Partitioned Algorithms,Classification Statistics based algorithms,Classification,Decision Trees,Fraud Detection,Association Analysis,Implementation data mining business applications with computer software,Text Mining,Genetic Algorithms and Fuzzy Logic,Artificial Neural Networks.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
1. Produces data warehouse from database.
12, 9
A
1.1. Explains datamining.
12, 9
A
1.2. Defines Data Warehouse.
12, 9
A
2. Relates Data Mining Models to each other.
12, 9
A
2.1. Explains data mining models.
12, 9
A
3. Applies the classification model.
12, 9
A
4. Applies clustering model.
12, 9
A
5. Employs the connection analysis model.
12, 9
A
6. Employs Data Mining Algorithms.
12, 9
A
Teaching Methods:
12: Problem Solving Method, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam
Course Outline
Order
Subjects
Preliminary Work
1
Introduction
Reading the relevant section from the book
2
Data warehouse and OLAP
Reading the relevant section from the book
3
Data Preparation for data analysis , data cleaning noise reduction.
Reading the relevant section from the book
4
Data mining task analysis problem description
Reading the relevant section from the book
5
Clustering and Partitioned Algorithms
Reading the relevant section from the book
6
Classification Statistics based algorithms
Reading the relevant section from the book
7
Classification
Reading the relevant section from the book
8
Decision Trees
Reading the relevant section from the book
9
Fraud Detection
Reading the relevant section from the book
10
Association Analysis
Reading the relevant section from the book
11
Implementation data mining business applications with computer software
Reading the relevant section from the book
12
Text Mining
Reading the relevant section from the book
13
Genetic Algorithms and Fuzzy Logic
Reading the relevant section from the book
14
Artificial Neural Networks
Reading the relevant section from the book
Resources
1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Veri Madenciliği, Papatya, Gökhan Sİlahtaroğlu 3. Veri Madenciliği Teori Uygulama ve Felsefesi, Papatya Bilim, İlker KÖSE.
will be provided by the lecturer.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
In light of the current developments in the field of business, have knowledge about the main sources, process and design which are related to the field.
2
With understanding relationships among concept, institution and method that related to disciplines of business management, establish a connection between them.
3
Reach scientific knowledge in the field of business; evaluate, and use this information to solve business problems.
4
Implement the idea about individual and social problems of business and in the light of current developments find resolutions.
5
Solve the problems encountered in management theory and practice by using research methods that specific to field.
6
Carry out an independent study which needs expertise in the field of management and its sub-disciplines.
7
Develop different perspectives and take responsibility to solve complex issues which require expertise and encountered in the field of business and its sub-disciplines.
8
Critically evaluate acquired knowledge and skills in the field of business and its sub-disciplines.
9
Develop a positive attitude towards lifelong learning and convert it to behavior.
10
Mean acquired knowledge in the field of business and current developments about management theory and practice to all groups systematically in written or oral form.
11
Critically question concepts and institutions of business, settled practice of management and norms and when necessary develop and attempt to change them.
12
Use software which in the level required by the field of business and information and communication technologies in the advanced level.
13
Take into consideration and internalize social, scientific and ethical values in the process of gathering, processing and evaluating information about field of management and its sub-disciplines.
14
With developing understanding of honesty, justice and ethics are required to be manager, teach around them.
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
50
Rate of Final Exam to Success
50
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
12
1
12
Resolution of Homework Problems and Submission as a Report
0
0
0
Term Project
14
6
84
Presentation of Project / Seminar
1
2
2
Quiz
0
0
0
Midterm Exam
1
10
10
General Exam
1
18
18
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
168
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(168/30)
6
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
BIG DATA ANALYSIS AND DECISION MAKING IN BUSINESS
-
Spring Semester
3+0
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
Turkish
Course Level
Second Cycle (Master's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Gökhan SİLAHTAROĞLU
Name of Lecturer(s)
Prof.Dr. Gökhan SİLAHTAROĞLU
Assistant(s)
Aim
To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms.
Course Content
This course contains; Introduction,Data warehouse and OLAP,Data Preparation for data analysis , data cleaning noise reduction.,Data mining task analysis problem description,Clustering and Partitioned Algorithms,Classification Statistics based algorithms,Classification,Decision Trees,Fraud Detection,Association Analysis,Implementation data mining business applications with computer software,Text Mining,Genetic Algorithms and Fuzzy Logic,Artificial Neural Networks.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
1. Produces data warehouse from database.
12, 9
A
1.1. Explains datamining.
12, 9
A
1.2. Defines Data Warehouse.
12, 9
A
2. Relates Data Mining Models to each other.
12, 9
A
2.1. Explains data mining models.
12, 9
A
3. Applies the classification model.
12, 9
A
4. Applies clustering model.
12, 9
A
5. Employs the connection analysis model.
12, 9
A
6. Employs Data Mining Algorithms.
12, 9
A
Teaching Methods:
12: Problem Solving Method, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam
Course Outline
Order
Subjects
Preliminary Work
1
Introduction
Reading the relevant section from the book
2
Data warehouse and OLAP
Reading the relevant section from the book
3
Data Preparation for data analysis , data cleaning noise reduction.
Reading the relevant section from the book
4
Data mining task analysis problem description
Reading the relevant section from the book
5
Clustering and Partitioned Algorithms
Reading the relevant section from the book
6
Classification Statistics based algorithms
Reading the relevant section from the book
7
Classification
Reading the relevant section from the book
8
Decision Trees
Reading the relevant section from the book
9
Fraud Detection
Reading the relevant section from the book
10
Association Analysis
Reading the relevant section from the book
11
Implementation data mining business applications with computer software
Reading the relevant section from the book
12
Text Mining
Reading the relevant section from the book
13
Genetic Algorithms and Fuzzy Logic
Reading the relevant section from the book
14
Artificial Neural Networks
Reading the relevant section from the book
Resources
1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Veri Madenciliği, Papatya, Gökhan Sİlahtaroğlu 3. Veri Madenciliği Teori Uygulama ve Felsefesi, Papatya Bilim, İlker KÖSE.
will be provided by the lecturer.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
In light of the current developments in the field of business, have knowledge about the main sources, process and design which are related to the field.
2
With understanding relationships among concept, institution and method that related to disciplines of business management, establish a connection between them.
3
Reach scientific knowledge in the field of business; evaluate, and use this information to solve business problems.
4
Implement the idea about individual and social problems of business and in the light of current developments find resolutions.
5
Solve the problems encountered in management theory and practice by using research methods that specific to field.
6
Carry out an independent study which needs expertise in the field of management and its sub-disciplines.
7
Develop different perspectives and take responsibility to solve complex issues which require expertise and encountered in the field of business and its sub-disciplines.
8
Critically evaluate acquired knowledge and skills in the field of business and its sub-disciplines.
9
Develop a positive attitude towards lifelong learning and convert it to behavior.
10
Mean acquired knowledge in the field of business and current developments about management theory and practice to all groups systematically in written or oral form.
11
Critically question concepts and institutions of business, settled practice of management and norms and when necessary develop and attempt to change them.
12
Use software which in the level required by the field of business and information and communication technologies in the advanced level.
13
Take into consideration and internalize social, scientific and ethical values in the process of gathering, processing and evaluating information about field of management and its sub-disciplines.
14
With developing understanding of honesty, justice and ethics are required to be manager, teach around them.
