Data Cleaning Techniques

Data cleaning is the process of identifying and rectifying errors, inconsistencies, and inaccuracies in a dataset. It is important in data science because it ensures that the data is accurate, reliable, and suitable for analysis, leading to more robust and trustworthy results.

Some common tasks involved in data cleaning include handling missing values, correcting typos, standardizing formats, removing duplicates, and addressing outliers.

Effective data cleaning enhances the quality of insights drawn from analysis by improving the accuracy and reliability of the data, leading to more reliable and trustworthy results.

<p>The first step in data cleaning is data assessment, which involves understanding the structure of the dataset, checking for data ranges, and evaluating the data volume.</p> Signup and view all the answers

<p>Evaluating the data volume during data cleaning is important to assess the number of records and the proportion of missing data, which can impact the reliability and completeness of the analysis.</p> Signup and view all the answers