The Data Incubator made its name with a model that stood out from the usual bootcamp crowd. Rather than selling an open enrolment course to anyone with a credit card, it ran a selective fellowship aimed at people who already held advanced degrees, often PhDs in quantitative fields, and wanted to move into industry data science. Admitted fellows could train at no cost because the business was built around placing them with hiring companies, which meant the incentives pointed at employment outcomes rather than course sales. Alongside that, it offered paid bootcamps and shorter courses for people who did not fit the fellowship profile.
It is now part of Pragmatic Institute, which has folded the training into a larger catalogue and added organisational structure. The content itself is solid and appropriately demanding, spanning data manipulation, statistics, machine learning, and the practical side of getting models into production, and the calibre of the cohort tends to be high because of who the programme selects for. That selectivity is also the main thing to understand before applying. This was never designed as a gentle introduction, and it shows.
If you already think in terms of distributions and gradients, the pace and level will suit you, and the hiring orientation is a real advantage. If you are a career changer without a quantitative background, you will likely find it either out of reach at admission or overwhelming once inside, and you would be better served building fundamentals through something more introductory first. The paid bootcamp also carries the usual bootcamp risk, a meaningful price tag whose payoff depends on a hiring market you do not control, so the outcome is less guaranteed than the marketing of any bootcamp suggests. My overall read is that this is a credible, rigorous programme for the specific person it was built for, the analytically strong graduate who wants a fast and employment focused route into the field, and a mismatch for almost everyone outside that description.