Data Science Foundations: Data Mining in R

Do you work in data analysis? Learn how to mine data using R. Topics include dimensionality reduction, clustering, classification, association analysis, and more.

Introduction

• R for data mining
• Who should watch this course
• 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
• Dataset: Handwritten digits
• PCA
• LDA
• t-SNE
• Challenge: PCA
• Solution: PCA

3. Clustering

• Clustering overview
• Dataset: Penguins
• Hierarchical clustering
• K-means
• DBSCAN
• Challenge: K-means
• Solution: K-means

4. Classification

• Classification overview
• Dataset: Spambase
• K-nn
• Naive Bayes
• Decision trees
• Challenge: K-nn
• Solution: K-nn

5. Association Analysis

• Association analysis overview
• Dataset: Groceries
• Apriori
• Eclat
• CBA
• Challenge: Apriori
• Solution: Apriori

6. Time-Series Mining

• Time-series mining overview
• Dataset: AirPassengers
• Time-series decomposition
• ARIMA
• MLP
• Challenge: Decomposition
• Solution: Decomposition

7. Text Mining

• Text mining overview
• Dataset: The Iliad
• Sentiment analysis: Binary classification
• Sentiment analysis: Sentiment scoring
• Visualizing Word pairs
• Challenge: Sentiment scoring
• Solution: Sentiment scoring

Conclusion

• Next steps