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.
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
-
Spring Semester
3+0
3
9
Course Program
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.
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.
