FOMO is the fear of missing out. FOBO is similar- the fear of a better option. FOBO gives a name to that spiral we fall into when we obsessively research every possible option when faced with a decision, fearing we’ll miss out on the “best” one.
When starting a new machine learning project, just the thought and the reality that we'll never be able to examine every possible algorithm, package, tool and/or technology before making a decision can be overwhelming and it can easily block us. What if we make the wrong decision and don't bring enough value? What if what we choose to use isn't "state-of-the-art"?
The first solutions that come to mind are often the “most-hyped” options, for example DL, although those are not always the best fitting ones. How should you decide what to use?

We will present a practical roadmap to guide your Data Science projects: What to focus on first (probably, it’s cleaning data and feature engineering), which algorithms to try first (hint: not NNs!!) and tips for convincing business leaders to focus on what works, not on the hype.