SOCIOLOGY
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	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)	To learn and compare major sociology perspectives, both classical and contemporary, and apply all of them to analysis of social conditions.
2)	To be able to identify the basic methodological approaches in building sociological and anthropological knowledge at local and global levels
3)	To be able to use theoretical and applied knowledge acquired in the fields of statistics in social sciences.
4)	To have a basic knowledge of other disciplines (including psychology, history, political science, communication studies and literature) that can contribute to sociology and to be able to make use of this knowledge in analyzing sociological processes
5)	To have a knowledge and practice of scientific and ethical principles in collecting, interpreting and publishing sociological data also develop ability how to share this data with experts and lay people, using effective communication skills
6)	To develop competence in analyzing and publishing sociological knowledge by using computer software for quantitative and qualitative analysis; and develop an attitute for learning new techniques in these fields.
7)	To identify and to have a knowledge of the theories related to urban and rural sociology and demography, and political sociology, sociology of gender, sociology of body, visual sociology, sociology of work, sociology of religion, sociology of knowledge and sociology of crime.
8)	To have knowledge of how sociology is positioned as a scientific discipline from a philosophical and historical perspective
9)	To have the awareness of social issues in Turkish society, to develop critical perspective in analysing these issues and to have a knowledge of the works of Turkish sociologists and to be able to transfer this knowledge
10)	To have the awareness of social issues and global societal processes and to apply sociological analysis to development and social responsibility projects
11)	To have the ability to define a research question, design a research project and complete a written report for various fields of sociology, either as an individual or as a team member.
12)	To be able to transfer the knowledge gained in the areas of sociology to the level of secondary school.