What is "data wrangling"?

Data wrangling refers specifically to the process of cleaning and organizing raw data in preparation for analysis. This involves several steps that may include transforming data formats, handling missing values, correcting inconsistencies, and structuring the dataset in a way that it can be effectively analyzed. The goal is to take unrefined data that may be messy or disorganized and convert it into a format that is suitable for modeling and analytics.

In contrast, collecting data from various sources is an essential first step in creating a dataset but does not encompass the tasks of refining and preparing that data for analysis. Advanced statistical techniques focus on the application of certain methods to analyze data but do not relate to the initial preparation phase. Lastly, automation of data reporting deals with streamlining the process of presenting data insights but does not involve the foundational steps of data preparation that data wrangling requires. Therefore, the correct choice accurately captures the essence of data wrangling as an essential precursor to effective data analysis.