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
Having advanced theoretical and practical knowledge supported by textbooks, application tools and other resources containing current information in the field
X
2
To have information about basic resources, current trends and approaches regarding Business Management in the light of current developments in the field of business.
X
3
Accessing and evaluating scientific knowledge in the field of Business Management and using this knowledge in solving high-level managerial problems of businesses.
4
Thinking about individual and social problems related to Business Management and producing solutions in the light of current developments
5
Solving the problems encountered in business theory and practice by using research methods specific to the field of Business.
6
Independently carry out a study that requires expertise in the field of Business Management and its sub-disciplines.
X
7
Developing different perspectives and producing solutions by taking responsibility for the solution of complex problems that require expertise and are encountered in the field of Business Management and its sub-disciplines.
8
Critically evaluate the expert knowledge and skills acquired in the field of Business Management and its sub-disciplines.
9
Developing positive attitudes towards lifelong learning and turning them into behavior
X
10
To systematically convey the expert knowledge gained in the field of business administration and its sub-disciplines and the current developments in management theory and practice, both verbally and in writing, to groups in the field and outside the field.
11
Critically questioning business concepts and institutions, established management practices and rules, and taking initiative to develop and change them when necessary.
12
Advanced use of information and communication technologies along with computer software at the level required by the Business Management field.
X
13
Internalizing information regarding Business Management fields and sub-disciplines by considering social, scientific and ethical values in the processes of acquiring, processing and evaluating.
14
Developing and teaching others the honesty, justice and ethics required to be a Senior Manager.
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
Having advanced theoretical and practical knowledge supported by textbooks, application tools and other resources containing current information in the field
X
2
To have information about basic resources, current trends and approaches regarding Business Management in the light of current developments in the field of business.
X
3
Accessing and evaluating scientific knowledge in the field of Business Management and using this knowledge in solving high-level managerial problems of businesses.
4
Thinking about individual and social problems related to Business Management and producing solutions in the light of current developments
5
Solving the problems encountered in business theory and practice by using research methods specific to the field of Business.
6
Independently carry out a study that requires expertise in the field of Business Management and its sub-disciplines.
X
7
Developing different perspectives and producing solutions by taking responsibility for the solution of complex problems that require expertise and are encountered in the field of Business Management and its sub-disciplines.
8
Critically evaluate the expert knowledge and skills acquired in the field of Business Management and its sub-disciplines.
9
Developing positive attitudes towards lifelong learning and turning them into behavior
X
10
To systematically convey the expert knowledge gained in the field of business administration and its sub-disciplines and the current developments in management theory and practice, both verbally and in writing, to groups in the field and outside the field.
11
Critically questioning business concepts and institutions, established management practices and rules, and taking initiative to develop and change them when necessary.
12
Advanced use of information and communication technologies along with computer software at the level required by the Business Management field.
X
13
Internalizing information regarding Business Management fields and sub-disciplines by considering social, scientific and ethical values in the processes of acquiring, processing and evaluating.
14
Developing and teaching others the honesty, justice and ethics required to be a Senior Manager.
