Sr. Data Scientist,
Antonio Loconte
Almost a decade turning data into predictive models.
Selected notes
Most numbers in analysis come with some degree of uncertainty, even when the final slide does not show it. A forecast can be wrong, an estimate can move with a different sample, and a relationship can weaken over time. Being clear about uncertainty does not make an analysis weaker; it makes it more useful.
Before celebrating a complex model, it helps to ask a basic question: is it actually better than a simple alternative? A baseline can be a historical average, a rule of thumb, or a straightforward model. It gives everyone a fair reference point and prevents complexity from being mistaken for progress.
Adding more columns to a dataset does not always improve the result. Some variables repeat the same information, some add noise, and some make a model harder to maintain. Dimensionality reduction is about finding a smaller set of signals that still captures the main structure of the problem.
A model that is too simple misses important patterns. A model that is too flexible can end up learning random detail from the training data. The bias–variance trade-off is the balance between those two problems. In practice, it is a reminder to test whether a model still works when the data changes.
Charts are part of the thinking process, not just the presentation at the end. A plot can reveal that two groups behave differently, that an assumption was wrong, or that a pattern is driven by only a few observations. The best charts make it easier to ask the next sensible question.
When a dataset has many variables, it is easy to create a chart that looks sophisticated but says very little. The useful question is simple: what do we want people to notice? Reducing dimensions, grouping similar features, or focusing on a few meaningful comparisons can make complex data easier to discuss without pretending that the complexity has disappeared.
A point that sits far away from the rest of the data deserves attention, but not panic. It could be a typing mistake, a broken sensor, an unusual customer, or an event that genuinely matters. Looking at a chart is often the quickest way to understand whether the number should be corrected, explained, or investigated.