BANKING AND INSURANCE MANAGEMENT (TURKISH) | |||||
Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 |
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. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Associate (Short 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. |
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 |
1.Introduction to Basic Concepts 2.Data Exploration 3.Classification 4.Clustering 5.Dimensionality Reduction 6.Frequent Item Set Mining 7.Association Rule Mining |
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 |
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 |
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 |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | To have the ability to understand the basic concepts of Banking and Insurance and to be able to use them effectively in business. | |
2) | To have the ability to work individually or in a team when needed on matters related to his/her profession and to follow and apply the developments in his/her sector. | |
3) | To be equipped with the necessary knowledge to carry out the legal responsibilities and to follow the related regulations in their sector. | |
4) | To understand the importance of banking and insurance from the point of the state’s economy and enterprises and to express this importance properly. | |
5) | To be able to use the computer as well as the profession requires and to be able to do work, accumulate knowledge and to use this knowledge relevantly and effectively. | |
6) | To make them gain the ability to find practical solutions for the problems of daily commercial activities and to take correct decisions. | |
7) | To be able to take responsibilities in banking and insurance sector and more generally in the finance sector and to be qualified to start his/her own business after the legal requirements have been met. | |
8) | To have the competency to carry out the accountancy related to banking and insurance. | |
9) | To have the competency to build effective customer relations and to have effective communication and persuasion skills. | |
10) | To be able to determine the accumulated knowledge druring the education in line with the cause and effect relations and to be able to have the necessary professional qualifications to know where, when and how to use his/her knowledge. |