Question

What does "Data Cleansing" mean? What are the best ways to practice this?

A) Adding noise to the data
B) Removing outliers and errors
C) Increasing the sample size
D) Conducting hypothesis tests

Answer 1

Data Cleansing is about correcting errors in data, specifically by removing outliers and inaccuracies, which improves data quality for analysis and decision-making.

Step-by-step explanation:

Data Cleansing refers to the process of identifying and correcting inaccuracies and inconsistencies in data to improve its quality. The best way to practice data cleansing would be option B) Removing outliers and errors, which involves tasks such as fixing typographical errors, ensuring consistency in data entry, and dealing with missing values.

It is crucial within the field of data science to ensure that analyses are performed on clean and reliable data. Common steps include collecting and organizing data, identifying and correcting outliers or incorrect values, standardizing data formats, and verifying that the data makes sense within the specific context. These steps are part of an iterative process often accompanied by additional techniques such as normalization and transformation for further refinement.