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Raw Data, Roles, Hypotheis, Traditional #, ML (Ensembles, Deep Learning),…
Raw Data
Metadata
Data Collection
Cleaning/ETL
Cleaned / Transformed Data #
Curate Data
Dataset
Modeling
Regression / ML Models
Model Validation
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Transform to Stational
Impute Missing Data
Create Dynamic Factor Features
Create Combo Features
Data Quality Ranking
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Data Catalog # # #
Master tables (id/tags)
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Per file: file level metadata: source, links, time (start/stop), date acquired, description, frequency, data quality, number of columns #
Per Column: header, description, data quality, data type, pct missing, value range, units
Data Catalog: Processed Data #
Per file: processing steps: Cleaning, Imputation, Transformation, Deletion #, sources or relational links, time intervals
Per Column: Imputation, Binning, Math Operations: normalization, Log, Power, Diff, Rounding
Data Catalog: Curated Data #
Per file: Hypotheis #, Sources
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