Data quality assurance is the process of making sure that data is accurate, complete, and reliable before it is used for decision-making or analysis. It involves checking for errors, inconsistencies, and missing information in data sets. This process helps organisations trust their data and avoid costly mistakes caused by using poor-quality data.
Feature engineering is the process of transforming raw data into meaningful inputs that improve the performance of machine learning models. It involves selecting, modifying, or creating new variables, known as features, that help algorithms understand patterns in the data. Good feature engineering can make a significant difference in how well a model predicts outcomes or…
Data pipeline automation is the process of setting up systems that move and transform data from one place to another without manual intervention. It involves connecting data sources, processing the data, and delivering it to its destination automatically. This helps organisations save time, reduce errors, and ensure that data is always up to date.
Data cleansing is the process of detecting and correcting errors or inconsistencies in data to improve its quality. It involves removing duplicate entries, fixing formatting issues, and filling in missing information so that the data is accurate and reliable. Clean data helps organisations make better decisions and reduces the risk of mistakes caused by incorrect…