Course Objectives:
- Gain a solid understanding of the fundamental concepts and techniques of data mining.
- Develop practical skills in data preprocessing, analysis, and visualization.
- Learn to apply classification, clustering, association analysis, anomaly detection, and dimensionality reduction techniques to real-world datasets.
- Complete a data mining project from start to finish, demonstrating the ability to extract valuable insights from data.
TextBook & Lecture Notes: “Introduction to Data Mining” by Tan, Steinbach, and Kumar (First Edition)
Week 1: Introduction to Data Mining
- Overview of Data Mining: Scope, definitions, and fundamental concepts.
- Importance and Applications of Data Mining.
Week 2: Data
- Understanding Types of Data.
- Data Quality and Preprocessing Techniques.
- Lab Session: Data preprocessing with Python.
Week 3: Exploring Data
- Techniques for Data Visualization.
- Summary Statistics for Understanding Data.
Week 4: Classification: Basic Concepts, Decision Trees, and Model Evaluation
- Introduction to Classification and Decision Trees.
- Model Evaluation Metrics.
- Lab Session: Implementing decision trees and evaluating model performance.
Week 5-6: Classification: Alternative Techniques
- Advanced Classification Algorithms: k-NN, SVM, Neural Networks.
- Comparison and Selection of Classification Techniques.
Week 7: Association Analysis: Basic Concepts and Algorithms
- Market Basket Analysis and the Apriori Algorithm.
- Project: Perform association analysis on a retail dataset, due in two weeks.
Week 8: Midterm Exam
Week 9: Association Analysis: Advanced Concepts
- Advanced Association Analysis Algorithms.
- Enhancements in Association Analysis.
- Lab Session: Implementing the FP-growth algorithm.
Week 10: Cluster Analysis: Basic Concepts and Algorithms
- Clustering Techniques and their Applications.
- Assignment: Clustering a dataset and analyzing the results.
Week 11: Anomaly Detection and Dimensionality Reduction
- Anomaly Detection: Concepts, Applications, and Techniques.
- Dimensionality Reduction: PCA, SVD, and t-SNE.
- Lab Session: Anomaly detection and implementing PCA.
Weeks 12-14: Project Presentations
- Students will present their final projects, which should incorporate the concepts and techniques learned throughout the course.
- Each presentation will include a discussion of the problem statement, methodology, data analysis, results, and conclusions.