PRIVATE SECURITY AND PROTECTION (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) | Upon the completion of the program, the students will be able to; Utilize the theoretical information they have acquired in private security sector | |
2) | Develop the skill of working in a team cooperatively | |
3) | Develop the skill of identifying and analyzing the vocational problems of private security and resolving them effectively. | |
4) | Develop the behavioral consciousness of occupational ethics and sense of responsibility | |
5) | Develop an awareness for life long learning and physical progress | |
6) | Develop the skill of having information about daily problems and developments about private security. | |
7) | Comprehend the laws and regulations of the sector. | |
8) | Develop the skill of effective communication. | |
9) | Develop the skill of adopting the security technologies. | |
10) | Develop the skill of planning and practicing vocational processes. | |
11) | Develop the skill of having entrepreneurship personality | |
12) | Develop the skill of communicating in the private security sector in a foreign language. |