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
Will be able to have advanced theoretical and practical knowledge supported by textbooks, application tools and other resources containing current information in the field.
2
With the current developments in the banking and finance sector, it acquires information about basic resources, current trends and approaches.
3
Reaches, evaluates and uses scientific information in the field of Banking and Finance and uses it in solving problems.
4
It carries out a work that requires expertise in the field of Banking and Finance and related disciplines independently and produces solutions.
5
Acquires the necessary theoretical background to understand and solve banking and finance problems and make theoretical contributions.
6
Analyze and synthesize financial and economic data. Presents, discusses and defends data both in writing and orally, both in academic and business life.
7
Gains detailed information on the global aspects of financial markets and their connections with international relations, and at the level of contributing to the existing information.
8
Evaluates the role and importance of social, regulatory and political factors for the banking and financial sector, both practically and theoretically.
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
Will be able to have advanced theoretical and practical knowledge supported by textbooks, application tools and other resources containing current information in the field.
2
With the current developments in the banking and finance sector, it acquires information about basic resources, current trends and approaches.
3
Reaches, evaluates and uses scientific information in the field of Banking and Finance and uses it in solving problems.
4
It carries out a work that requires expertise in the field of Banking and Finance and related disciplines independently and produces solutions.
5
Acquires the necessary theoretical background to understand and solve banking and finance problems and make theoretical contributions.
6
Analyze and synthesize financial and economic data. Presents, discusses and defends data both in writing and orally, both in academic and business life.
7
Gains detailed information on the global aspects of financial markets and their connections with international relations, and at the level of contributing to the existing information.
8
Evaluates the role and importance of social, regulatory and political factors for the banking and financial sector, both practically and theoretically.
