2.2 Perform feature engineering.
Identify and extract features from data sets, including from data sources such as text, speech,
image, public datasets, etc.
Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic
features, 1 hot encoding, reducing dimensionality of data)
2.3 Analyze and visualize data for machine learning.
Graphing (scatter plot, time series, histogram, box plot)
Interpreting descriptive statistics (correlation, summary statistics, p value)
Clustering (hierarchical, diagnosing, elbow plot, cluster size)