Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
3	CPP209	Data Mining	3+0+0	3	4	23.11.2024

Course Details

Language of Instruction	Turkish
Level of Course Unit	Associate Degree
Department / Program	Computer Programming
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	To acquire basic information about the basic concepts of Data Mining and other disciplines (mathematics, statistics, data visualization, etc.) that data mining is related to.
Course Content	Basic information about Data Mining, the most used data mining algorithms and their applications.
Course Methods and Techniques	Theory and Practice
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Instructor Tuğba Kavak tugba.kavak@gedik.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Lecture Notes Data Mining with Python, CANER ERDEN, 2021.
Course Notes	Lecture Presentations

Course Category

Mathematics and Basic Sciences	%10
Field	%90

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	3	42
Hours for off-the-c.r.stud	14	2	28
Mid-terms	1	10	10
Final examination	1	20	20
Total Work Load			Number of ECTS Credits 4 100

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Will be able to make basic definitions of data mining and explain basic data mining problems.
2	By a data set, it will be able to perform various analyzes such as data preprocessing, data visualization, summary statistics, etc.
3	Will be able to explain the basic concepts of Regression and Classification and use various techniques.
4	Will be able to explain the basic concepts of clustering, will be able to perform clustering analysis.
5	Students will have knowledge about how Python programming language is used in data mining and will improve their previous Python programming skills.
6	Will have knowledge about basic statistical concepts and will be able to perform introductory statistical analysis.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Review of Python programming knowledge, remembering the use of numpy, pandas and matplotlib libraries
2	Introduction to Data Mining, Concepts, Application Areas, Variable Types, Big Data
3	Knowledge Discovery and Stages, Data preprocessing and making the data ready for model building
4	Basic statistical concepts, statistical analysis, outlier analysis, feature selection
5	Types of Learning, Regression and Error Computation
6	Practical Examples of Regression
7	General review of the topics covered so far this week, question-answer with students
8	Midterm
9	Introduction to classification, binary and multiclass classification, confusion matrix construction, use of error metrics
10	Other Classification Algorithms (Definition and Application)
11	Introduction to clustering, use of K-Means and DBSCAN algorithms, Elbow method and Silhouette analysis
12	Introduction to Text Mining, Data Scraping, using Beautiful Soup library, vectorizing texts, dimension reduction, bag-of-words etc. applications
13	Text Summarization, similarity in texts and angular cosine similarity
14	Association Analysis, Association Rules, Support and Confidence Criteria, etc.
15	Review of all topics covered so far this week and question-answer
16	Final exam

Contribution of Learning Outcomes to Programme Outcomes

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=237567&curProgID=44&lang=en