Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
BUSINESS INTELLIGANCE AND STRATEGY | YSTD1213483 | Spring Semester | 3+0 | 3 | 9 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | Turkish |
Course Level | Third Cycle (Doctorate Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
Assistant(s) | |
Aim | The aim of this course is to provides a broad category of practices and technologies for accessing, collecting, storing, analyzing, sharing and accessing data so that students can make better managerial decisions. |
Course Content | This course contains; Business Intelligence an Introduction,Business Intelligence Essentials:,Business Intelligence Types,Architecting the Data,Introduction to Data Mining:,
Data Mining Techniques,Introduction to Data Warehousing,Different Ways of Data Warehousing,Knowledge Management,Business Intelligence Life Cycle,Business Intelligence User Model,Business Intelligence Issues and Challenges,Business Intelligence Strategy and Road Map,Implementing Business Intelligence. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
0. For all outcomes | 14, 4, 6, 9 | A, E |
1. Explains the concept of business intelligence and the business intelligence value chain. | | |
2. will be able to explain the capabilities of business intelligence at the basic level. | | |
3. Explains the types and roles of business intelligence in businesses. | | |
4. Explains data types and the basis of data architecture. | | |
5. Performs data preprocessing and dirty data cleaning. | | |
6. Explains the definition and necessity of data mining. | | |
6.1. Knows the machine learning. | | |
7. Explains data mining techniques. | | |
7.1. Performs classification and decision trees structure and cluster analysis. | | |
8. Defines the data warehouse and its components. | | |
8.1. Explains the concept of OLAP and data modeling. | | |
9. Explains the data warehousing. | | |
9.1. Uses B2B and B2C business intelligence models. | | |
Teaching Methods: | 14: Self Study Method, 4: Inquiry-Based Learning, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Business Intelligence an Introduction | The chapter related to the book will be read. |
2 | Business Intelligence Essentials: | |
3 | Business Intelligence Types | |
4 | Architecting the Data | |
5 | Introduction to Data Mining: | Related chapter in the course notes should be read. |
6 |
Data Mining Techniques | Related chapter in the course notes should be read. |
7 | Introduction to Data Warehousing | Related chapter in the course notes should be read. |
8 | Different Ways of Data Warehousing | |
9 | Knowledge Management | |
10 | Business Intelligence Life Cycle | |
11 | Business Intelligence User Model | |
12 | Business Intelligence Issues and Challenges | |
13 | Business Intelligence Strategy and Road Map | |
14 | Implementing Business Intelligence | |
Resources |
Business Intelligence Guidebook: From Data Integration to Analytics 1st Edition by Rick Sherman |
1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Data Mining Concepts and Techniques , J. Han & M. Kamber, Morgan Kaufman. 3. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results , Bernard Marr, Wiley, 2016 4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil ,2017 5. Naked Statistics: Stripping the Dread from the Data, Charles Wheelan, 2013 |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | Defines the theoretical issues in the field of management. | | | | | |
2 | Describes the necessary mathematical and statistical methods in the field of management. | | | X | | |
3 | Uses at least one computer program in the field of management. | | | X | | |
4 | Gains the necessary research skills to conduct academic studies. | | | | | |
5 | Student has the ability of time management. | X | | | | |
6 | Adopts the principles of scientific ethics and scientific responsibility. | X | | | | |
7 | Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies. | | X | | | |
8 | Uses theoretical and practical knowledge in the field of management. | | | | | |
9 | To be able to produce or interpret an original work by making at least one scientific study related to the field and expanding the boundaries of knowledge in the field. | | X | | | |
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 | 10 | 10 | 100 |
Resolution of Homework Problems and Submission as a Report | 2 | 16 | 32 |
Term Project | 1 | 30 | 30 |
Presentation of Project / Seminar | 0 | 0 | 0 |
Quiz | 1 | 6 | 6 |
Midterm Exam | 1 | 18 | 18 |
General Exam | 1 | 30 | 30 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 258 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(258/30) | 9 |
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 |
---|
BUSINESS INTELLIGANCE AND STRATEGY | YSTD1213483 | Spring Semester | 3+0 | 3 | 9 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | Turkish |
Course Level | Third Cycle (Doctorate Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
Assistant(s) | |
Aim | The aim of this course is to provides a broad category of practices and technologies for accessing, collecting, storing, analyzing, sharing and accessing data so that students can make better managerial decisions. |
Course Content | This course contains; Business Intelligence an Introduction,Business Intelligence Essentials:,Business Intelligence Types,Architecting the Data,Introduction to Data Mining:,
Data Mining Techniques,Introduction to Data Warehousing,Different Ways of Data Warehousing,Knowledge Management,Business Intelligence Life Cycle,Business Intelligence User Model,Business Intelligence Issues and Challenges,Business Intelligence Strategy and Road Map,Implementing Business Intelligence. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
0. For all outcomes | 14, 4, 6, 9 | A, E |
1. Explains the concept of business intelligence and the business intelligence value chain. | | |
2. will be able to explain the capabilities of business intelligence at the basic level. | | |
3. Explains the types and roles of business intelligence in businesses. | | |
4. Explains data types and the basis of data architecture. | | |
5. Performs data preprocessing and dirty data cleaning. | | |
6. Explains the definition and necessity of data mining. | | |
6.1. Knows the machine learning. | | |
7. Explains data mining techniques. | | |
7.1. Performs classification and decision trees structure and cluster analysis. | | |
8. Defines the data warehouse and its components. | | |
8.1. Explains the concept of OLAP and data modeling. | | |
9. Explains the data warehousing. | | |
9.1. Uses B2B and B2C business intelligence models. | | |
Teaching Methods: | 14: Self Study Method, 4: Inquiry-Based Learning, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Business Intelligence an Introduction | The chapter related to the book will be read. |
2 | Business Intelligence Essentials: | |
3 | Business Intelligence Types | |
4 | Architecting the Data | |
5 | Introduction to Data Mining: | Related chapter in the course notes should be read. |
6 |
Data Mining Techniques | Related chapter in the course notes should be read. |
7 | Introduction to Data Warehousing | Related chapter in the course notes should be read. |
8 | Different Ways of Data Warehousing | |
9 | Knowledge Management | |
10 | Business Intelligence Life Cycle | |
11 | Business Intelligence User Model | |
12 | Business Intelligence Issues and Challenges | |
13 | Business Intelligence Strategy and Road Map | |
14 | Implementing Business Intelligence | |
Resources |
Business Intelligence Guidebook: From Data Integration to Analytics 1st Edition by Rick Sherman |
1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Data Mining Concepts and Techniques , J. Han & M. Kamber, Morgan Kaufman. 3. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results , Bernard Marr, Wiley, 2016 4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil ,2017 5. Naked Statistics: Stripping the Dread from the Data, Charles Wheelan, 2013 |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | Defines the theoretical issues in the field of management. | | | | | |
2 | Describes the necessary mathematical and statistical methods in the field of management. | | | X | | |
3 | Uses at least one computer program in the field of management. | | | X | | |
4 | Gains the necessary research skills to conduct academic studies. | | | | | |
5 | Student has the ability of time management. | X | | | | |
6 | Adopts the principles of scientific ethics and scientific responsibility. | X | | | | |
7 | Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies. | | X | | | |
8 | Uses theoretical and practical knowledge in the field of management. | | | | | |
9 | To be able to produce or interpret an original work by making at least one scientific study related to the field and expanding the boundaries of knowledge in the field. | | X | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 50 |
Rate of Final Exam to Success | | 50 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 04/01/2024 - 17:11Son Güncelleme Tarihi: 04/01/2024 - 17:11
×- A-Z Programs
- Undergraduate
- Graduate
- Academic Calendar
- Double Major & Minor Programs
- Erasmus
- Prospective Students
- Registration
- Re-Enrolment
- Fees
- Directorate of Registrar’s Office
- FAQ
- Accommodation
- Scholarships
- Lateral and Vertical Transfer
- Summer School
- Preparation
- Transportation