Data Mining
Undergraduate and Master's Course, Istanbul Aydın University, Department of Software Engineering, 2019
This course was taught to both undergraduate (SEN 431) and master’s students across multiple Spring and Fall semesters.
It provided theoretical and practical knowledge on data mining techniques, covering both classical methods and modern machine learning approaches using R and Python.
Course Level:
- Undergraduate: SEN 431 – Data Mining
- Graduate: Data Mining for MSc in Artificial Intelligence
Years Taught:
- Fall 2019
- Spring 2020
- Fall 2020
- Spring 2021
- Spring 2022
Key Topics Covered:
- Data preprocessing and feature selection
- Classification algorithms (Decision Trees, SVM, k-NN, Naïve Bayes)
- Clustering methods (k-means, hierarchical clustering, DBSCAN)
- Association rule mining (Apriori, FP-Growth)
- Dimensionality reduction (PCA, t-SNE)
- Evaluation metrics (accuracy, precision, recall, F1-score, ROC)
- Ensemble methods (Random Forest, Boosting)
- Applications in biomedical datasets and text mining
Tools & Languages Used:
- R (
caret,randomForest,e1071,arules) - Weka (for early-stage concepts and visualization)
- Jupyter Notebook for practical assignments and reporting