Data Science Foundations: Data Mining in R

Description

In this course, you will :

• Data mining is the branch of data science that focuses on discovering actionable patterns in large and diverse datasets: clusters of similar customers, long-term trends that can only be identified after disentangling seasonal and random effects, and new methods for predicting important outcomes.
• focuses on data mining in R, presents a wide range of algorithms, including machine learning methods, and provides critical information on data mining laws and policies.
• provides a high-level overview of dimensionality reduction
• introduces clustering, including hierarchical clustering, before moving on to association analysis
• Describes time-series mining and decomposition before concluding with text mining, sentiment analysis, and sentiment scoring.

Syllabus :

1. Preliminaries

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

 

2. Dimensionality Reduction

• Dataset: Handwritten digits
• PCA
• LDA
• t-SNE

 

3. Clustering

• Dataset: Penguins
• Hierarchical clustering
• K-means
• DBSCAN

 

4. Classification

• Dataset: Spambase
• K-nn
• Naive Bayes
• Decision trees

 

5. Association Analysis

• Dataset: Groceries
• Apriori
• Eclat
• CBA

 

6. Time-Series Mining

• Dataset: AirPassengers
• Time-series decomposition
• ARIMA
• MLP

 

7. Text Mining

• Dataset: The Iliad
• Sentiment analysis: Binary classification
• Sentiment analysis: Sentiment scoring
• Visualizing Word pairs

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