| Data Cleaning | Data Wrangling |
| Definition: Data cleaning specifically focuses on identifying and correcting errors or inaccuracies in the dataset. Tasks: Involves handling missing values, correcting typos, standardizing formats, and removing duplicates to ensure data accuracy. Goal: Improve data quality by eliminating errors, inconsistencies, or outliers that could impact the validity of analysis or machine learning models. Methods: Encompasses techniques like imputation, outlier detection, and standardization to enhance the accuracy and reliability of the data. Tools: Utilizes tools similar to those in data wrangling, with a focus on cleaning and validating the integrity of the dataset. | Definition: Data wrangling involves the overall process of collecting, transforming, and organizing raw data into a more usable and structured format. Tasks: Includes tasks such as merging datasets, handling missing values, reshaping data structures, and extracting relevant features. Goal: Prepare the data for analysis by addressing inconsistencies, transforming variables, and creating a dataset suitable for modeling. Methods: Involves data aggregation, merging, reshaping, and other operations to make data more manageable and conducive to analysis. Tools: Utilizes tools like pandas in Python, dplyr in R, or SQL for data manipulation. |
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