Civil Engineering (English)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

BIL394

Course Name:

Data Mining

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	7

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. PINAR YILDIRIM

Course Lecturer(s):

Dr. BİLİNMİYOR BEKLER
Prof. Dr. PINAR YILDIRIM

Course Assistants:

Course Objective and Content

Course Objectives:	The purpose of the course is to educate students about the main concepts and methods of data mining.
Course Content:	The course contains these topics: classification, clustering, association algorithms and data mining studies in different areas.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Ability to explain the concepts of data mining
2) Ability to apply data preprocessing techniques
3) Ability to use the algorithms of data mining.
4) Ability to use the tools of data mining
5) Ability to produce solutions in the subjects of data mining
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	• What Motivated Data Mining? Why Is It Important? • So, What Is Data Mining? • Data Mining—On What Kind of Data? • Data Mining Functionalities—What Kinds of Patterns Can Be Mined?	Reading chapter 1
2)	• What is data? • Attributes. • Types of attributes. • Discrete and continuous variables. • Types of data set. • Record data. • Data matrix. • Document data. • Transaction data. • Graph data. • Chemical data. • Ordered data. • Why data preprocessing? • Why is data dirty? • Why is data preprocessing important? • Multi-dimensional measure of data quality. • Major tasks in data preprocessing. • Data quality. • Noise. • Outliers.	Reading chapter 2.
3)	• Missing values. • Duplicate data. • Mining data descriptive characteristics. • Measuring the central tendency. • Symmetric vs. skewed data. • Properties of normal distribution curve. • Histogram analysis. • Positively and negatively correlated data. • Not correlated data. • Data cleaning. • How to handle missing data? • How to handle noisy data? • Simple discretization methods: Binning. • Regression. • Cluster analysis. • Data cleaning as a process. • Aggregation. • Sampling. • Types of sampling. • Sample size.	Reading chapter 2.
4)	• Classification. • Illustrating classification task. • Examples of classification task. • Classification techniques. • Example of a decision tree. • Another example of decision tree. • Apply model to test data. • Decision tree induction. • Issues: data preparation. • Issues: evaluating classification methods. • Algorithm for decision tree induction(ID3/C4.3). • Attribute selection measure: Information gain. • Decision tree example.	Reading chapter 8.
5)	• Numeric variables and missing values. • Overfitting and tree pruning. • Enhancements to basic decision tree induction. • Model evaluation. • Metrics for performance evaluation. • Limitation of accuracy. • Cost matrix. • Calculation of accuracy. • Cost-sensitive measures. • Model evaluation. • Methods for performance evaluation. • Methods of estimation. • ROC (Receiver Operating Characteristic). • Instance Based Classification. • Nearest neighbor classification. • k-Nearest neighbor algorithm example.	Reading chapter 8-9.
6)	• What is cluster analysis? • Applications of cluster analysis • What is not cluster analysis? • Notion of a cluster can be ambiguous. • Types of clustering. • Characteristics of the input data are important. • Clustering algorithms. • Hierarchical clustering. • Agglomerative clustering algorithm. • Cluster distance measures. • Single link(min) hierarchical clustering. • Single link(min) hierarchical clustering example.	Reading chapter 10.
7)	• Complete link(max) hierarchical clustering example. • K-means clustering. • Importance of choosing initial centroids. • Limitations of k-means. • Overcoming k-means limitations. • K-means clustering example.	Reading chapter 10.
8)	Midterm1
9)	• Association rule mining • Frequent itemset • Association rule • Association rule mining task • • Apriori algorithm • Apriori algorithm example	Reading chapter 6.
10)	• Statistical classification models. • Bayes theorem and classifier. • Bayes classifier example. • Continuous variables.	Reading chapter 6.
11)	• Text and web mining. • Natural language processing. • Part-of-speech tagging. • Word sense disambiguation. • Text databases and IR. • Indexing techniques. • Types of text data mining. • Text classification. • Document clustering. • Text categorization. • Categorization methods. • Vector space model.
12)	Midterm2
13)	Project presentations
14)	Project presentations
15)	Final exam

Sources

Course Notes / Textbooks:	Data Mining Concept and Techniques J.Han and M.Kamber @2012\| Morgan Kaufmann Publishers ISBN 978-0-12-381479-1
References:	Yok / None

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2) Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
4) Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.
5) Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.
6) Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8) Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9) Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices.
10) Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
11) Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
4)	Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.
5)	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.
6)	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7)	Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8)	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9)	Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices.
10)	Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
11)	Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.

Learning Activity and Teaching Methods

Lesson
Project preparation

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Individual Project

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 10
Midterms	2	% 50
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	16	48
Project	2	40
Midterms	12	100
Final	4	32
Total Workload		220