Data Science Foundations: Data Mining

Introduction

• Python for data mining
• What you should know
• Exercise files

1. Preliminaries

• Tools for data mining
• The CRISP-DM data mining model
• Privacy, copyright, and bias
• Validating results

2. Dimensionality Reduction

• Dimensionality reduction overview
• Handwritten digits dataset
• PCA
• LDA
• t-SNE
• Challenge: PCA
• Solution: PCA

3. Clustering

• Clustering overview
• Penguin dataset
• Hierarchical clustering
• K-means
• DBSCAN
• Challenge: K-means
• Solution: K-means

4. Classification

• Classification overview
• Spambase dataset
• KNN
• Naive Bayes
• Decision trees
• Challenge: KNN
• Solution: KNN

5. Association Analysis

• Association analysis overview
• Groceries dataset
• Apriori
• Eclat
• FP-Growth
• Challenge: Apriori
• Solution: Apriori

6. Time-Series Mining

• Time-series mining
• Air Passengers dataset
• Time-Series decomposition
• ARIMA
• MLP
• Challenge: Decomposition
• Solution: Decomposition

7. Text Mining

• Text mining overview
• Iliad dataset
• Sentiment analysis: Binary classification
• Sentiment analysis: Sentiment scoring
• Word pairs
• Challenge: Sentiment scoring
• Solution: Sentiment scoring

Conclusion

• Next steps