Prerequisites

• Basic programming skills (Python recommended)
• Fundamental mathematics knowledge
• Basic statistics understanding
• Linear algebra basics
• Computer with minimum 8GB RAM
• Recommended: Basic machine learning concepts

Course Syllabus

Week 1: Data Mining Foundations

• Introduction to data mining
• Knowledge discovery process
• Data mining applications
• Machine learning integration
• Interdisciplinary perspectives
• Data mining task overview
• Ethical considerations

Week 2: Data Preprocessing

• Data cleaning techniques
• Handling missing values
• Data transformation methods
• Feature engineering
• Dimensionality reduction
• Principal component analysis
• Data compression strategies

Week 3: Exploratory Data Analysis

• Statistical analysis fundamentals
• Visualization techniques
• Pattern discovery methods
• Density estimation
• Outlier detection
• Similarity assessment
• Data storytelling principles

Week 4: Classification Techniques

• Decision tree algorithms
• Neural network classification
• Bayesian classification
• Support vector machines
• Rule-based classification
• Performance evaluation metrics
• Model optimization strategies

Week 5: Clustering Methods

• Partitioning algorithms
• Hierarchical clustering
• Density-based clustering
• Grid-based approaches
• Similarity measurement
• Advanced clustering techniques
• Cluster validation methods

Week 6: Association Rule Mining

• Frequent itemset generation
• Market basket analysis
• Apriori algorithm
• Sequential pattern mining
• Graph pattern mining
• Rule generation techniques
• Scalable mining approaches

Week 7: Advanced Pattern Discovery

• Constraint-based mining
• Spatiotemporal pattern analysis
• Trajectory pattern recognition
• Sub-graph pattern extraction
• Complex pattern identification
• Mining diverse pattern types
• Performance optimization

Week 8: Machine Learning Integration

• Predictive modeling techniques
• Feature selection strategies
• Ensemble learning methods
• Cross-validation approaches
• Model performance evaluation
• Advanced classification techniques
• Practical implementation strategies

Week 9: Specialized Mining Techniques

• Web content mining
• Text mining fundamentals
• Spatial data mining
• Multimedia data analysis
• Web structure mining
• Web usage mining
• Domain-specific mining approaches

Week 10: Big Data Mining

• Distributed mining techniques
• Scalable algorithm design
• Large-scale data processing
• Cloud computing integration
• Performance optimization
• Parallel mining strategies
• Advanced computational techniques

Week 11: Ethical and Practical Considerations

• Data privacy techniques
• Bias detection
• Responsible mining practices
• Regulatory compliance
• Ethical decision-making
• Professional standards
• Real-world implementation challenges

Week 12: Capstone Project

• End-to-end data mining project
• Industry best practices
• Professional networking
• Career guidance
• Portfolio development
• Advanced problem-solving
• Final project presentations

Learning Outcomes

• Comprehensive data mining expertise
• Advanced pattern discovery skills
• Practical implementation knowledge
• Ethical data analysis understanding
• Industry-ready mining capabilities
• Strategic problem-solving techniques
• Professional portfolio creation

Certification

Students receive a comprehensive Data Mining certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging technological trends.