ECTS - Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery (MAN332) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Data Mining and Knowledge Discovery | MAN332 | Area Elective | 2 | 1 | 0 | 2.5 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| 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, Demonstration, 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 | 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 | 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 | 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 | Basic literature on K-means algorithm and Silhouette score should be examined. |
| 6 | Relationship Rules Mining - Apriori algorithm, Market basket analysis | 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 | 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) | 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 | 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. Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel , Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, 2019 |
|---|---|
| Other Sources | 2. Andres Fortino, Data Mining and Predictive Analytics for Business Decisions: A Case Study Approach, Mercury Learning and Information, 2023. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 15 | 1 |
| 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 | 85 |
| Percentage of Semester Work | 75 |
|---|---|
| Percentage of Final Work | 25 |
| 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 | Acquiring the skills of understanding, explaining, and using the fundamental concepts and methods of economics | |||||
| 2 | Acquiring the skills of macro level economic analysis | |||||
| 3 | Acquiring the skills of micro level economic analysis | |||||
| 4 | Understanding the formulation and implementation of economic policies at the local, national, regional, and/or global level | |||||
| 5 | Learning different approaches on economic and related issues | |||||
| 6 | Acquiring the quantitative and/or qualitative techniques in economic analysis | |||||
| 7 | Improving the ability to use the modern software, hardware and/or technological devices | |||||
| 8 | Developing intra-disciplinary and inter-disciplinary team work skills | |||||
| 9 | Acquiring an open-minded behavior through encouraging critical analysis, discussions, and/or life-long learning | |||||
| 10 | Adopting work ethic and social responsibility | |||||
| 11 | Developing the skills of communication. | |||||
| 12 | Improving the ability to effectively implement the knowledge and skills in at least one of the following areas: economic policy, public policy, international economic relations, industrial relations, monetary and financial affairs. | |||||
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 | ||