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 | Natural & Applied Sciences Master's Degree |
| 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(Textbook 1) |
| 2 | Introduction to data mining | Chapter 1 (Textbook 1) |
| 3 | Data, measurements, and data preprocessing | Chapter 2 (Textbook 1) |
| 4 | Data warehousing and online analytical processing (OLAP) | Chapter 3 (Textbook 1) |
| 5 | Pattern mining: basic concepts and methods | Chapter 4 (Textbook 1) |
| 6 | Pattern mining: basic concepts and methods (cont.) | Chapter 4 (Textbook 1) |
| 7 | Classification: basic concepts and methods | Chapter 6 (Textbook 1) |
| 8 | Classification: basic concepts and methods (cont.) | Chapter 6 (Texbook 1) |
| 9 | Cluster analysis: basic concepts and methods | Chapter 8 (Textbook 1) |
| 10 | Cluster analysis: basic concepts and methods (cont.) | Chapter 8 (Textbook 1) |
| 11 | Outlier detection & Project Discussions | Chapter 11 (Textbook 1) |
| 12 | Data warehousing and mining practical applications-1 | Applications on software |
| 13 | Data warehousing and mining practical applications-2 | Applications on software |
| 14 | Data warehousing and mining practical applications-3 | Applications on software |
| 15 | Final Examination Period | Review of topics |
| 16 | Final Examination Period | Review of topics |
Sources
| Course Book | 1. Han, J., Kamber, M. & Pei, Jian (2023) Data Mining: Concepts and Techniques (4th edition) Morgan Kaufmann, Elsevier: Cambridge. MA |
|---|---|
| Other Sources | 2. Bhatia, P. (2019) Data Mining and Data Warehousing: Principles and Practical Techniques Cambridge: Cambridge, UK |
| 3. Taniar, D. & Rahay, W. (2021) Data warehousing and analytics: Fueling the Data Engine Springer Nature: Switzerland AG | |
| 4. Sharda, R., Delen, D. & Turban, E. (2020) Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th edition / Global edition) Pearson, London, UK |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | 1 | 15 |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 3 | 10 |
| Presentation | - | - |
| Project | 1 | 20 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 7 | 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 | Applies knowledge of mathematics, science, and engineering | X | ||||
| 2 | Designs and conducts experiments, analyzes and interprets experimental results. | X | ||||
| 3 | Designs a system, component, or process to meet specified requirements. | X | ||||
| 4 | Works effectively in interdisciplinary fields. | |||||
| 5 | Identifies, formulates, and solves engineering problems. | X | ||||
| 6 | Has awareness of professional and ethical responsibility. | |||||
| 7 | Communicates effectively. | X | ||||
| 8 | Recognizes the need for lifelong learning and engages in it. | X | ||||
| 9 | Has knowledge of contemporary issues. | X | ||||
| 10 | Uses modern tools, techniques, and skills necessary for engineering applications. | X | ||||
| 11 | Has knowledge of project management skills and international standards and methodologies. | X | ||||
| 12 | Develops engineering products and prototypes for real-world problems. | X | ||||
| 13 | Contributes to professional knowledge. | X | ||||
| 14 | Conducts methodological and scientific research. | X | ||||
| 15 | Produces, reports, and presents a scientific work based on original or existing knowledge. | X | ||||
| 16 | Defends the original idea generated. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | 1 | 4 | 4 |
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 10 | 10 |
| Report | |||
| Homework Assignments | 3 | 2 | 6 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 125 | ||