DATA ASCEND GROUP

Data Preparation

The data preparation process is a critical step that lays the foundation for effective analysis and modelling. It involves a series of tasks aimed at cleaning, transforming, and structuring raw data into a usable format for analysis. Initially, data is collected from various sources, often in diverse formats and quality levels. During data preparation, we carefully examine and clean the data, addressing issues such as missing values, outliers, and inconsistencies. We may also perform feature engineering to create new variables or transform existing ones to enhance their relevance. Data is then organised into structured datasets, which may involve merging, reshaping, or aggregating data points. This process ensures that the data is consistent, accurate, and ready for statistical analysis or machine learning algorithms. Effective data preparation is not only crucial for the reliability of our insights but also saves time and resources in the downstream analysis, leading to more robust and meaningful results.