What does 'data munging' or 'data wrangling' primarily involve?

Data munging, also known as data wrangling, primarily involves the processes of cleaning and organizing data to make it suitable for analysis. This is a crucial step in the data science pipeline, as raw data is often messy, inconsistent, and incomplete. Data munging entails techniques such as removing duplicates, handling missing values, transforming data types, and structuring data in a way that facilitates further analysis.

The importance of this process cannot be overstated; effective data munging ensures that subsequent analyses and predictive modeling yield accurate and reliable results. Without proper cleaning and organization, any conclusions drawn or predictions made could be fundamentally flawed.

While other choices might seem relevant in the broader context of data science, they do not directly define data munging. For instance, running statistical tests or building predictive models requires a clean dataset, but those activities occur after the data has been adequately wrangled. Similarly, documenting data insights reflects the reporting and interpretation phase of data analysis, which also comes after data wrangling has taken place. Therefore, cleaning and organizing data are the core activities that define the concept of data munging.