Levels of Data Measurement

It is widely reported that over 80% of a data scientist’s time is spent cleaning and engineering data. Great effort is put into preparing the information that will feed a data scientist’s models. Including irrelevant information or messy data in the modeling cycle can lead to models that are inaccurate or show false insights.

As such, one of the first steps of data cleaning is understanding what features or attributes will be available for the work and what type of attribute they will be. Data can be measured on four main scales: Interval, Ratio, Nominal, and Ordinal. Knowing the…