The Philosophical Principles of Exploratory Data Analysis (EDA)

Exploratory data analysis (EDA) is a crucial step in any data science project. It involves analyzing raw data to extract meaningful insights and discover hidden patterns. The purpose of EDA is not only to find significant correlations and relationships but also to understand the underlying structure of data. In this article, we will delve into the philosophical principles that guide EDA and the assumptions that should be kept in mind when performing data analysis.

The Philosophical Principles of EDA

EDA is a philosophical approach to data science that aims to uncover underlying patterns and relationships in data. It involves interacting with data and exploring different aspects of it to find useful insights. The following philosophical principles guide EDA:

1. Empathy

Empathy is the ability to understand and share the feelings of others. In EDA, it involves understanding the data and the people who generated it. EDA requires empathizing with the data and the context in which it was collected to gain insights that are relevant and meaningful to the problem at hand.

2. Curiosity

Curiosity is the driving force behind EDA. It involves asking questions and exploring different aspects of the data to understand how it works. Curious explorers are the ones who discover hidden patterns and relationships in the data.

3. Creativity

Creativity is essential in EDA to come up with new interpretations and insights. EDA involves exploring different data visualization techniques and generating new hypotheses to test. It requires a creative approach to data analysis to uncover hidden insights.

4. Humility

Humility is crucial in EDA to avoid overfitting and underfitting. EDA requires treating data with respect and recognizing its limitations. Data analysts should be open to learning from their mistakes and accept that their analysis may have flaws.

The Assumptions of EDA

EDA assumes that data is existence, measurable, and at some point, it can be collected. The following are the assumptions of EDA:

1. Data is Existence

EDA assumes that data is a real and tangible object that can be observed, measured, and analyzed. Data can be perceived through various senses, such as sight, sound, and touch. Data analysts assume that data is a way of representing the world, and it is possible to understand the world better through data analysis.

2. Data is Measurable

EDA assumes that data can be measured and quantified. Measuring data involves assigning numerical values to different aspects of it. Data analysts assume that data can be assigned values that reflect its properties, such as height, weight, and temperature.

3. Data is Collected

EDA assumes that data is collected systematically and objectively. Data collectors are assumed to follow a set of rules and procedures to ensure data accuracy and consistency. Data analysts assume that data collectors are impartial and unbiased and that their methods are transparent and reproducible.

4. Data is Noisy

EDA assumes that data is noisy and has errors. EDA involves filtering out noise and identifying outliers in the data. Data analysts assume that errors in data can be reduced through statistical techniques such as mean filtering and median filtering.

Conclusion

EDA is a philosophical approach to data science that guides data analysts in understanding the underlying patterns and relationships in data. The philosophical principles of EDA are empathy, curiosity, creativity, and humility. Additionally, EDA assumes that data is existence, measurable, collected, and noisy. EDA is an essential step in any data science project, and it requires a creative and curious approach to data analysis. Data analysts should be empathetic and humble when analyzing data and should strive to uncover meaningful insights that are relevant and useful to the problem at hand.