There are several approaches to data analysis. The most popular ones that are relevant to this book are the following:
- Classical data analysis: For the classical data analysis approach, the problem definition and data collection step are followed by model development, which is followed by analysis and result communication.
- Exploratory data analysis approach: For the EDA approach, it follows the same approach as classical data analysis except the model imposition and the data analysis steps are swapped. The main focus is on the data, its structure, outliers, models, and visualizations. Generally, in EDA, we do not impose any deterministic or probabilistic models on the data.
- Bayesian data analysis approach: The Bayesian approach incorporates prior probability distribution knowledge into the analysis steps as shown in the following diagram. Well, simply put, prior probability distribution of any quantity expresses the belief about that particular quantity before considering some evidence. Are you still lost with the term prior probability distribution? Andrew Gelman has a very descriptive paper about prior probability distribution. The following diagram shows three different approaches for data analysis illustrating the difference in their execution steps:
Data analysts and data scientists freely mix steps mentioned in the preceding approaches to get meaningful insights from the data. In addition to that, it is essentially difficult to judge or estimate which model is best for data analysis. All of them have their paradigms and are suitable for different types of data analysis.