In data analytics, it is important to understand the different data preparation phases. Two crucial phases in this process are data preparation and data cleaning. Let’s take a closer look at the nuances of these two terms.
Data cleaning refers to the process where errors and inconsistencies in data are identified and subsequently corrected or removed, thereby enhancing its quality.
This stage encompasses tasks such as handling missing values, smoothing noisy data, detecting and removing outliers, and sorting out inconsistencies in the data.
Some standard techniques involved in data cleaning are:
The ultimate goal of data cleaning is to curate a clean, consistent, accurate dataset, ready to undergo deeper analysis.
Data wrangling, a broader process, involves transforming and mapping data from a “raw” format into another, facilitating more convenient data consumption and analysis.
Data wrangling is a comprehensive process incorporating a wide range of activities, including but not limited to data cleaning. It may involve data transformation, data enrichment, and even data visualization.
The techniques often used in data wrangling include:
Data wrangling aims to transform data into a structured format, making it significantly easier to analyze and facilitating its use in data modeling or machine learning tasks.
While data cleaning can be seen as a subset of data wrangling, it is essential to note that it involves a broader set of operations on data. Data cleaning focuses mainly on enhancing data quality, while data wrangling is about transforming and mapping data into a more analyzable and usable format.