BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| BUSINESS ADMINISTRATION | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP4336 | Introduction to Data Mining | Spring Fall |
3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Non-Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Recommended Optional Program Components: | None |
| Course Objectives: | In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis. |
Learning Outcomes
|
The students who have succeeded in this course; 1. Be able to understand Data Gathering and Pre-processing 2. Become familiar with Frequent Item Set Detection 3. Be able to understand Association Rule Mining 4. Be able to understand Classifiers, and their benefits 5. Be able to use Clustering 6. Be able to understand Clustering Evaluation |
Course Content
| 1.Introduction to Basic Concepts 2.Data Exploration 3.Classification 4.Clustering 5.Dimensionality Reduction 6.Frequent Item Set Mining 7.Association Rule Mining |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction to Basic Concepts | None |
| 2) | Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis | None |
| 3) | Data Pre-Processing, Transformation, Normalization, Standardization | None |
| 4) | Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation | None |
| 5) | Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers | |
| 6) | Classification | None |
| 6) | Classification and Regression: Artificial Neural Networks, Support Vector Machines | |
| 7) | Midterm I | Review of all topics covered so far |
| 8) | Clustering: Partitioning and Hierarchical Algorithms | None |
| 9) | Clustering: Density-Based Algorithms | |
| 10) | Cluster Evaluation, Comparing Clusterings | None |
| 11) | Midterm II | none |
| 12) | Dimensionality Reduction | none |
| 13) | Frequent Item Set Mining | none |
| 14) | Association Rule Mining | none |
Sources
| Course Notes / Textbooks: | Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar |
| References: | Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 2 | % 20 |
| Project | 1 | % 20 |
| Midterms | 2 | % 20 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 16 | 32 |
| Project | 5 | 15 |
| Homework Assignments | 6 | 12 |
| Midterms | 8 | 28 |
| Final | 6 | 26 |
| Total Workload | 155 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Being able to identify problems and ask right questions | |
| 2) | Having problem solving skills and developing necessary analytical attitude | |
| 3) | Comprehending theoretical arguments along with counter arguments in detail | |
| 4) | Gaining awareness of lifelong learning and being qualified for pursuing graduate education | |
| 5) | Applying theoretical concepts in project planning | |
| 6) | Communicating efficiently by accepting differences and carrying out compatible teamwork | |
| 7) | Increasing efficiency rate in business environment | |
| 8) | Developing innovative and creative solutions in face of uncertainty | |
| 9) | Researching to gather information for understanding current threats and opportunities in business | |
| 10) | Being aware of the effects of globalization on society and business while deciding | |
| 11) | Possessing digital competence and utilizing necessary technology | |
| 12) | Communicating in at least one foreign language in academic and daily life | |
| 13) | Possessing managing skills and competence | |
| 14) | Deciding with the awareness of the legal and ethical consequences of business operations | |
| 15) | Expressing opinions that are built through critical thinking process in business and academic environment |