Raw data is typically messy and requires a of transformations before it becomes useful for modeling and analysis work. Such Datasets can have missing data, duplicate records, corrupted data, incomplete records, and so on. In its simplest form, data munging, or data wrangling, is basically the transformation of raw data into a usable format. In most projects, this is the most challenging and time-consuming step.
However, without data munging your project can reduce to a garbage-in, garbage-out scenario.
Typically, you will execute a bunch of functions and processes such as subset, filter, aggregate, sort, merge, reshape, and so on. In addition, you will also do type conversions, add new fields/columns, rename fields/columns, and so on.
A large project can comprise of several different kinds of data with varying degrees of data quality. There can be a mix of numerical, textual, time-series, structured, and unstructured data including audio and video data used together...
