What is the correct progression of steps when using Accelerated Data Science?

The correct progression of steps when using Accelerated Data Science begins with Data Acquisition, followed by Exploratory Data Analysis and Visualization, then Model Training, and finally Feature Engineering.

Starting with Data Acquisition is essential because it involves collecting all relevant data from various sources, which forms the foundation of any data science project. Once the data is collected, Exploratory Data Analysis (EDA) and Visualization help in understanding the characteristics and patterns within the data. This step is crucial for identifying trends, anomalies, and potential issues that may need addressing before moving on to model training.

After conducting EDA, Model Training can take place. At this stage, data scientists use the insights gained from EDA to select appropriate algorithms and techniques for their machine learning models. Finally, Feature Engineering is performed, which involves creating new input features or modifying existing features to improve model performance based on the understanding gained from the previous steps.

This logical flow helps streamline the data science process, ensuring that each phase builds on the previous one, and the final model is robust and effective.