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PredictorX1 t1_iztv3pj wrote

I've never been at a workplace which used any of the structures you mention. Honestly, model development is fairly straightforward from the project management and software development perspectives. The clever bit is the statistics/machine learning, and the parts requiring the most care are data acquisition (problem definition, statistical sampling, ...), model validation (error resampling, testing for important sub-populations, ...) and deployment (verifying the deployed model, ...). Most serious analysts I know use something that resembles CRISP.

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acardosoj t1_j080weh wrote

CRISP is not a project management methodology, but more like a process. You would still need a project management methodology to manage resources.

We usually apply CRISP-DM (ML) within an agile framework.

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PredictorX1 t1_j082k9y wrote

>CRISP is not a project management methodology...

That was my point: Data science work needs a technical procedure, not project management.

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acardosoj t1_j08ap5c wrote

If you are working on a data science project, you would inevitably have project management activities in place. You need to report progress, need to manage costs, resources, schedule. You can do those in an ad hoc way without structure. But I guess that would lead to problems.

Imagine being asked for costs and progress estimates by a C-level. You would only be able to answer her if you keep track of these things. That's project management!

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PredictorX1 t1_j08cat9 wrote

In my experience, data science features costs which are relatively stable, and whose payment is committed to on an ongoing basis as a necessary part of the business by management. The only time costs would come into question is when more people were to be hired, on a permanent basis. Tracking the activity itself is handled by a manager of a small team, who periodically presents results to upper management. The only real "project management" I see is done in small teams when management assigns tasks and deploys or reports results to external entities. Tracking of progress is, again, in my experience, a light activity. I just don't perceive the need for excessive formality in the management of data science.

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Hyper1on t1_j0a80ym wrote

Usually you just make some estimates of projected costs, resource use, and timelines at the start of the project (aiming to be an overestimate), and if you are up to date with the progress made it's trivial to just correct these estimates if someone asks you for them.

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