Data exploration, bias-correction, harmonization, encoding, formatting, transformation, utility assessment, and data pipelines for achieving data readiness. Includes topics for data preparation and cleaning, data wrangling, data remediation, data munging, and ETL.
There’s a lot of work to get the data ready. AI can be powerful for clinical decision support, but requires big data which requires a lot of data management effort. Use the following table to assess and communicate how ready your data is.
A Python list comprehension is a very convenient syntax for generating simple lists that can replace loops and .append calls to make your code more succinct and readable.
The resources below provide a lot of help for getting started. Here's an example of how a list comprehension can be used to replace a lot of repetitive code with a reusable function:
