ECTS - Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery (ISL332) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Data Mining and Knowledge Discovery | ISL332 | Area Elective | 2 | 1 | 0 | 2.5 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | Turkish |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Discussion, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | The main purpose of this course is to learn the basic concepts and techniques of data mining and knowledge discovery. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | This course introduces fundamental concepts and techniques in the field of data mining and knowledge discovery within a business-oriented framework. Students will explore topics such as data collection, preprocessing, exploratory data analysis, classification, clustering, and association rule mining. They will also learn how to apply these techniques to solve business problems. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Data Mining - Basic concepts, data mining process model (CRISP-DM) and its stages, Data types, data sources, data mining examples in the business world | General information on data mining and basic concepts should be acquired. |
| 2 | Data Collection and Basic Data Preprocessing – Data collection methods and data quality, data cleaning, normalization and transformation and practises | Academic articles and projects regarding data mining concepts should be examined. |
| 3 | Exploratory Data Analysis (EDA) – Data visualization tools, Basic statistical analyzes and data summarization and practises | Resources on data visualization tools and basic statistical analysis should be examined. |
| 4 | Introduction to Classification Techniques - Basic classification algorithms (Decision trees, kNN), Classification performance metrics and practises | Learn about the basic principles of decision trees and kNN algorithms. |
| 5 | Introduction to Clustering Techniques - K-means algorithm, Evaluation of clustering results: Silhouette score, clustering examples for customer segmentation and practises | Basic literature on K-means algorithm and Silhouette score should be examined. |
| 6 | Relationship Rules Mining - Apriori algorithm, Market basket analysis and practises | Basic concepts of Apriori algorithm and market basket analysis should be examined. |
| 7 | Decision Support Systems and Data Mining - Integration of data mining with decision support systems and use of data mining results for business strategies and practises | Articles on the integration of decision support systems and data mining should be examined. |
| 8 | Midterm Exam | The covered topics should be reviewed. |
| 9 | Big Data and Businesses - Introduction to the concept of big data: Its role and importance in businesses, general information about big data technologies (Hadoop, Spark) and practises | Learn about the concept of big data and Hadoop and Spark technologies. |
| 10 | Simple Machine Learning Techniques - Introduction to machine learning: Supervised and unsupervised learning, Simple regression and classification models and practices | Basic information about the concepts of supervised and unsupervised learning should be examined. |
| 11 | Data Mining and Ethics - Ethics in data mining and data privacy, Information about data protection laws and ethical data mining practices | Must read on ethics and data privacy issues in data mining. |
| 12 | Data Mining Project Initiation - Planning and management in data mining projects, Project cycle, resource management and risk analysis and project topic selection | There should be a review of planning and management in data mining projects. |
| 13 | Applied Project Work - Working and guidance on projects chosen by students, Evaluation of data collection, pre-processing and analysis steps | Preparation of data mining projects prepared by students throughout the semester. |
| 14 | Project Presentations | Presentation of data mining projects prepared by students. |
| 15 | Project Presentations | Presentation of data mining projects prepared by students. |
| 16 | Final Exam | The course topics should be reviewed, and preparation for the final exam should be completed. |
Sources
| Course Book | 1. Filiz Ersöz, Veri madenciliği Teknikleri ve Uygulamaları, Seçkin Yayıncılık, 2023. |
|---|---|
| Other Sources | 2. Necati Cemaloğlu ve Ayhan Duykuluoğlu, Sosyal Bilimlerde Veri Madenciliği, Pegem Yayınları, 2020. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 15 | 16 |
| Laboratory | - | - |
| Application | 8 | 24 |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | 1 | 5 |
| Project | 1 | 10 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 25 |
| Toplam | 27 | 100 |
| Percentage of Semester Work | 75 |
|---|---|
| Percentage of Final Work | 25 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | X |
| 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 | Having the conceptual and applied knowledge about the basic functions of the business and gaining the ability to use the knowledge | |||||
| 2 | To be able to diagnose the problems related to the field of business, to use scientific approaches effectively in the solution of problems and decision-making processes. | X | ||||
| 3 | To be able to reveal and analyze the environmental, social, global effects and legal consequences of the applications related to the basic functions of the business. | |||||
| 4 | To be able to analyze information and reports that concern businesses at the national, regional and global level, to set strategic goals according to the results obtained | |||||
| 5 | Gain the ability to use, report and interpret Business Information Systems and sub-modules required by business management | X | ||||
| 6 | Planning necessary activities such as starting a new business with innovative and creative thinking and taking risks for the sustainability of the business, finding resources, making market analysis, preparing a business plan, and using the learned information in this direction. | |||||
| 7 | Being aware of the fact that the research and learning process continues throughout life, to be able to follow the scientific and technological developments related to the business, to support oneself and the organization in innovation and continuous development. | |||||
| 8 | To have the necessary leadership and managerial skills to achieve business objectives effectively and efficiently. | |||||
| 9 | To be able to conduct scientific research in the field of business and report the results of the research to be used in managerial decision-making processes. | X | ||||
| 10 | To be able to transfer information about the field of business using effective verbal, written and visual communication methods in the language of learning and professional English | |||||
| 11 | To be aware of professional ethics, environmental awareness, sustainability, social responsibility, cultural, social and universal values | |||||
| 12 | Working effectively with different disciplines or multicultural teams, taking responsibility, making risk analysis, adapting to change, critical thinking and using initiative in problem solving | |||||
| 13 | . | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | 8 | 1 | 8 |
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 1 | 14 |
| Presentation/Seminar Prepration | 1 | 2 | 2 |
| Project | 1 | 8 | 8 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 125 | ||