PhD in Mechatronic Engineering (English) with a bachelor's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

AIE509

Course Name:

Data Mining and Big Data

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. PINAR YILDIRIM

Course Lecturer(s):

Dr.Öğr.Üyesi SİNA ALP

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

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems
2) Ability to design mechatronic products and systems using the mechatronics approach
3) Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach
4) Ability to communicate effectively and teamwork with other disciplines
5) Understanding of performing engineering in accordance with ethical principles
6) Understanding of using technology with awareness of local and global socioeconomic impacts
7) Approach to knowing and fulfilling the necessity of lifelong learning

Course - Learning Outcome Relationship

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

	Program Outcomes	Level of Contribution
1)	Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems
2)	Ability to design mechatronic products and systems using the mechatronics approach
3)	Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach
4)	Ability to communicate effectively and teamwork with other disciplines
5)	Understanding of performing engineering in accordance with ethical principles
6)	Understanding of using technology with awareness of local and global socioeconomic impacts
7)	Approach to knowing and fulfilling the necessity of lifelong learning

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	70
Midterms	12	130
Final	4	62
Total Workload		310