Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
6YBS312Data Mining3+0+036

Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Management Information Systems
Mode of Delivery Face to Face
Type of Course Unit Compulsory
Objectives of the Course Teaching basic concepts such as what data mining is, why it is important, which problems it offers solutions to.
Course Content 1. Introduction and Basic Concepts:
- What is Data Mining?
- Importance and application areas of Data Mining.
- Data Mining process and stages.
2. Data Preprocessing:
- Data collection and data cleaning.
- Missing data processing and filling methods.
- Abnormal data detection and correction methods.
3. Supervised Learning:
- Supervised learning and classification algorithms.
- Regression analysis and modelling.
4. Unsupervised Learning:
- Unsupervised learning and clustering algorithms.
- Association rules and relational learning.
5. Model Selection and Validation:
- Validation techniques and evaluation of model performance.
- Hyperparameterisation and model selection.
6. Feature Engineering and Dimensionality Reduction:
- Feature selection and identification of important features.
- Dimension reduction techniques and applications.
7. Data Visualisation
- Data visualisation tools and techniques.
- Reporting and presentation skills.
8. Applications
- Examination of real world applications and industrial scenarios.
Course Methods and Techniques Lecture-Practice
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof. Volkan Oban
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • Data Mining Concepts and Techniques, Jiawei Han,Micheline Kamber, Jian Pe, The Morgan Kaufmann Series in Data Management Systems,Third Edition
Data Mining: Practical Machine Learning Tools and Techniques" by Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal.
Python Data Science Handbook" by Jake VanderPlas
R for Data Science" by Hadley Wickham & Garrett Grolemund
Data Mining Concepts and Techniques, Jiawei Han,Micheline Kamber, Jian Pe, The Morgan Kaufmann Series in Data Management Systems,Third Edition Provides a comprehensive introduction to basic data mining concepts and definitions.
Bir grup projesi
Vize ve Final Sınavları

Course Category
Mathematics and Basic Sciences %0
Engineering %50
Engineering Design %20
Social Sciences %0
Education %0
Science %0
Health %0
Field %30

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
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Project 1 % 0
Final examination 1 % 60
Total
3
% 100

 
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 3 42
Mid-terms 1 29 29
Final examination 1 40 40
Total Work Load   Number of ECTS Credits 6 153

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Ability to understand data mining concepts and techniques
2 Ability to apply data preprocessing and cleaning techniques
3 Ability to use basic data mining methods such as classification, clustering and association rules
4 Ability to use R and Python in data mining projects
5 Ability to perform effective analyzes on data sets and interpret the results
6 Demonstrate how to apply data mining results to business decisions
7 Ability to analyze complex data sets and extract information


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Data Mining • Concepts, importance, application areas. Introduction to R and Python.
2 Data Preprocessing • Data cleaning, transformation, normalization. Applications with R and Python.
3 Supervised Learning: Regression Analysis • Linear and logistic regression. Applications with R and Python.
4 Supervised Learning: Classification • Decision trees, support vector machines, random forests. Applications with R and Python.
5 Unsupervised Learning: Clustering • K-means, hierarchical clustering, DBSCAN. Applications with R and Python.
6 Unsupervised Learning: Association Rules • Apriori, FP-Growth algorithms. Applications with R and Python.
7 Anomaly Detection • Outlier detection, isolation forests and local outlier factor. Applications with R and Python.
8 Midterm exam
9 Model Selection and Validation • Model evaluation metrics, cross-validation. Applications with R and Python.
10 Feature Engineering and Reduction • Feature selection, dimensionality reduction techniques. Applications with R and Python.
11 Data Visualization and Reporting • Data exploration and visualization techniques. Applications with R and Python.
12 Real World Data Mining Applications • Sectoral application examples: e-commerce, social media analysis, customer segmentation. Real life examples with R and Python
13 Working on Course Projects • Students work on group projects, analyzing data sets and developing models.
14 Project Presentations and General Review of the Course • Presentation of the projects that students worked on throughout the semester. General review of the lesson and going over important points.


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8
All 3 4 4 5 4 3 4 2
C1 3 4 2 5 4 3 5 2
C2 2 4 3 4 3 3 4 2
C3 3 4 4 5 4 3 5 3
C4 2 5 5 4 3 2 3 3
C5 3 4 4 5 4 3 5 2
C6 2 3 3 4 3 3 3 2
C7 3 5 4 4 4 3 4 3

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