ECTS - Data Warehousing and Mining
Data Warehousing and Mining (ISE314) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Data Warehousing and Mining | ISE314 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| CMPE341 |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objectives of this course are to introduce and describe data warehousing steps and methods for accessing and analyzing warehouse data; and to introduce the basic concepts and rule mining techniques and develop skills of using recent data mining software for solving practical problems. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Data warehousing fundamentals, planning, design and implementation and administration of data warehouses, data cube computation, OLAP query processing; fundamentals of data mining and relationship with data warehouse and OLAP systems; association rule mining; algorithms for clustering, classification and rule learning. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to data warehousing | Chapter 1,2 (Textbook 1) |
| 2 | Dimensional data modeling | Chapter 2 (Textbook 2) |
| 3 | Building the data warehouse 1 | Chapter 6 (Textbook 1) |
| 4 | Building the data warehouse 2 | Chapter 6 (Textbook 1) |
| 5 | Building the data warehouse 3 | Chapter 6 (Textbook 1) |
| 6 | Data mining and data visualization 1 | Chapter 3 (Textbook 1) |
| 7 | Data mining and data visualization 2 | Chapter 3 (Textbook 1) |
| 8 | Data mining techniques: Clustering 1 | Chapter 5 (Other sources 3) |
| 9 | Data mining techniques: Decision trees 3 | Chapter 5 (Other sources 3) |
| 10 | Practical data warehousing and data mining 1 | Applications on software |
| 11 | Practical data warehousing and data mining 2 | Applications on software |
| 12 | Practical data warehousing and data mining 3 | Applications on software |
| 13 | Practical data warehousing and data mining 4 | Applications on software |
| 14 | Practical data warehousing and data mining 5 | Applications on software |
| 15 | Final Examination Period | Review of topics |
| 16 | Final Examination Period | Review of topics |
Sources
| Course Book | 1. George M. Marakas, “Modern Data Warehousing, Mining, and Visualization: Core Concepts”, Prentice Hall, 2003. |
|---|---|
| 2. R. Kimball and M. Ross, “The Data Warehouse Toolkit” , 2002, Wiley | |
| Other Sources | 3. Han J.W., Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006. |
| 4. Tan P.N., Steinbach M., Kumar V. Introduction to Data Mining. Addison Wesley, 2005. | |
| 5. Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 1 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. | |||||
| 2 | The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. | X | ||||
| 3 | The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. | X | ||||
| 4 | The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively. | X | ||||
| 5 | The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. | X | ||||
| 6 | The ability to work effectively in inter/inner disciplinary teams; ability to work individually | |||||
| 7 | Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. | |||||
| 8 | The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. | |||||
| 9 | Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology. | |||||
| 10 | The ability to behave according to ethical principles, awareness of professional and ethical responsibility; | |||||
| 11 | Knowledge of the standards utilized in software engineering applications | |||||
| 12 | Knowledge on business practices such as project management, risk management and change management; | X | ||||
| 13 | Awareness about entrepreneurship, innovation | |||||
| 14 | Knowledge on sustainable development | |||||
| 15 | Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety; | |||||
| 16 | Awareness of the legal consequences of engineering solutions | |||||
| 17 | An ability to describe, analyze and design digital computing and representation systems. | X | ||||
| 18 | An ability to use appropriate computer engineering concepts and programming languages in solving computing problems. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 5 | 80 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 20 | 20 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 15 | 15 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 135 | ||