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 | 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 | Have an advanced level of basic theoretical knowledge on the field of public finance in order to obtain the practical gains. | |||||
| 2 | Identify the issues related to the field of public finance by dealing with it within the framework of the methodological approach, and report and evaluate it from an analytical point of view. | |||||
| 3 | Understand, interpret and analyse economic and financial events, equipped with knowledge of certain disciplines, especially economics, business and law. | |||||
| 4 | Develop policies and strategies for solving the problems by establishing the cause-effect relationship related to fiscal and economic issues through theoretical information and the discussions. | |||||
| 5 | Establish the relationship of public financial management and budget theory with public policies, he/she makes strong budget analysis, develop analysis on public finance and makes a link with the policy implementation | |||||
| 6 | Understand the tax theory, learning the legal structure, following the legal and financial developments and gaining a professional competence in tax matters effectively develop it. | |||||
| 7 | Have knowledge of accounting systems in private and public institutions and businesses, analyze and interpret the financial and financial structure of institutions with the knowledge and competence gained. | |||||
| 8 | Gain knowledge of macroeconomic framework and growth theory, including theoretical and country examples, evaluates economic developments from a theoretical perspective. | |||||
| 9 | Use foreign language in financial and economic fields, follow international literature, communicate on professional issues. | |||||
| 10 | Benefit from technological developments in studies specific to its field by using information technologies, digital developments and common software. | |||||
| 11 | Use qualitative and quantitative methods for the analysis of economic, financial, social and institutional events. | |||||
| 12 | While fulfilling its academic and professional responsibilities, develop an approach that respects s United Nations sustainable development goals, freedoms, rights of the disadvantaged groups, environment, cultural and moral values. | |||||
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 | ||