Just looking at data and descriptive statistics is one of the first things a person is taught in machine learning, data science and statistics coursework. It’s

Just looking at data and descriptive statistics is one of the first things a person is taught in machine learning, data science and statistics coursework. It’s a major skill in the field that is emphasized all the time. Practitioners frequently do cursory data analysis and data exploration to gain insight into the data, corner cases and which modeling approaches are plausible. Just to give some examples, Bayesian Data Analysis (Gelman et al), Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman and Hill), Doing Bayesian Data Analysis (Kruschke), Deep Learning (Goodfellow, Bengio, Courville), Pattern Recognition and Machine Learning (Bishop) and the excellent practical blog post [0] by Karpathy all list graphical data checking, graphical goodness of fit investigation, descriptive statistics and basic data exploration as critical parts of the model building workflow. If you are seeing people produce models without this, it’s likely because companies try to have engineers do this work, or hire from bootcamps or other sources that don’t produce professional statisticians with grounding in proper professional approach to these problems. When people mistakenly think models are commodities you can copy paste from some tutorials, and don’t require real professional specialization, then yes, you get this kind of over-engineered outcome with tons of “modeling” that’s disconnected from the actual data or stakeholder problem at hand. [0]: https://karpathy.github.io/2019/04/25/recipe/ https://karpathy.github.io/2019/04/25/recipe/