What is a good response to the following post made by a classmate? Data wrangling is the process of transforming and mapping raw data into a usable format for analysis. Data wrangling can be time-consuming due to several factors. Raw datasets may contain errors and missing values that need to be identified and corrected. Cleaning and organizing data can be a meticulous task. Also, merging multiple datasets can be complex. Data wrangling offers benefits. By addressing the errors/missing values, it ensures data quality, which will lead to more accurate and reliable analysis. Data wrangling facilitates the extraction of insights by preparing and structuring the data for downstream analysis. Overall the time spent on data wrangling is an essential investment in pricing quality data analysis. Critically review the transactional data and provide any observations about the data that may limit the value for analysis. Below are my observations on the Sales by Hour Analysis: The top three (3) sales months in 2017 were December ($6,078), August ($5,468), and September ($5,299). The top three (3) lowest months in 2017 were January ($3,422), February ($3,467), and November ($3,773) Limitations to fully analyze the data set is due to missing values under multiple columns