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Data Science Foundations: Data Mining in R

via LinkedIn Learning

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Overview

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

Syllabus

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

Taught by

Barton Poulson

Reviews

4.8 rating at LinkedIn Learning based on 154 ratings

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