What is data wrangling?

Data wrangling is a process that data scientists and data engineers use to locate new data sources and convert the acquired information from its?raw data format?to one that is compatible with automated and semi-automated analytics tools.

Data wrangling, which is sometimes referred to as data munging, is arguably the most time-consuming and tedious aspect of data analytics. The exact tasks required in data wrangling depend on what?transformations?the analyst requires to make a dataset useable. The basic steps involved in data wranging include:

Discovery --?learn what information is contained in a data source and decide if the information has value.

Structuring --?standardize the data format for disparate types of data so it can be used for downstream processes.

Cleaning?-- remove incomplete and redundant data that could skew analysis.

Enriching?-- decide if you have enough data or need to seek out additional internal and/or 3rd-party sources.

Validating?-- conduct tests to expose data quality and consistency issues.

Publishing?-- make wrangled data available to stakeholders in downstream projects.

In the past, wrangling required the analyst to have a strong background in scripting languages such as?Python?or?R. Today, an increasing number of data wrangling tools use machine learning (ML) algorithms to carry out wrangling tasks with very little human intervention.