What does data science primarily use to develop new modeling methods?

Data science primarily relies on raw data to develop new modeling methods. This is because raw data provides the foundational evidence and real-world information necessary for analysis. Data scientists gather, clean, and preprocess this data to uncover patterns, trends, and insights that can inform the creation of statistical and machine learning models. The process of transforming raw data into meaningful insights is a core element of data science, allowing practitioners to understand relationships in the data and develop predictive models based on empirical evidence.

While personal opinions, trends from social media, and client surveys can provide some context or additional insights, they are not the primary source for developing new modeling methods. Instead, they may serve as supplementary information to complement the findings derived from raw data. The emphasis on raw data underscores the importance of data integrity and reliability in creating effective and accurate models in the field of data science.