Inventory data used and select biomedical datasets

[k8hertweck]

Data inventory from @lakikowolfe 👍 Note data and characteristics of the data

• How the data was used
• dtypes
• missing data?
• Include dummy datasets TA made

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Class 1

• Commute Time Dataset
  • Feature engineering and EDA
  • No missing data
  • Generic dataset with both categorical and numeric data

Class 2

• Commute Time Dataset
  • Viz of single variables and relationships, linear regression, mean squared error, random forests

Class 3

• Dummy dataset of 0 and 1 as an example of categorical data
• Dummy dataset of two random clouds of points to illustrate decision boundaries
• Tennis dataset
  • all categorical variables, target variable is yes/no played tennis
• Iris dataset
  • All numeric variables except for target variable (categorical: species)
• Dummy dataset for random forest

Class 4

• Dummy data to show the curse of dimensionality
• Iris dataset to show the benefits of PCA
  • Pair plot
  • PCA
• Dummy data to superimpose the first component line over a series of random points
• Dummy data and custom code to illustrate eiganvectors
• Centered faces dataset: "Eigenfaces"
• Dummy dataset of clusters to show K means
• Arrests data
  • four numeric vars
• NCI60 for PCA and hierarchical clustering

[k8hertweck]

Streamlining approach relative to R ML course.

Class 1: glaucoma data

• predicting age against other demographic data (linear regression)

Class 2: glaucoma data

• classification
• train/test
• imbalanced data
• pre-processing pipelines
• build workflows

Class 3: genomic data?

• regression
• imbalance
• workflow

Class 4: genomic data?

• unsupervised
• clustering
• PCA/UMAP