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Some more info for context; I studied Economics and Business Maths/Stats in college. I have been doing PM work since day 1 in ACN and there is only so many plans and status reports I can tolerate.
I would enjoy a new challenge, particularity one around data and analytics as I am confident with figures however I don’t even know where to start and what kind of roles to look for. Any help would be greatly appreciated.
Are you within a PM practice, or is this somehow just a skillset that you've been pegged to within your group? If the former, start trying to network out - I'm sure there's plenty of other practices within ACN doing other work (recognize that this is a bad time to try to move around...)
If the latter, start networking around for other roles that aren't PM.
Finally, make sure you have a decent understanding of any sort of analytics work you might be trying to do. A decent chunk of the non specialized analytics work is based on excel/Tableau instead of the fancy ML stuff, so it's vital to understand what you're moving towards and managing expectations appropriately
Rising Star
There are Python classes too but unless you are using new code, memory fades fast. I did a SQL/Python Udacity course, I suspect it’s mostly forgotten >1 year later, but I’d probably (re)learn fast.
Rising Star
We are always looking for people with applied maths, physics, engineering and like in the Applied Intelligence group. We are in S & C. Start by looking for people in that practise group. Also look for projects that have analytics as part of the SOW, typically because the roles will be posted. Even if those roles are filled, the SMs and MDs selling those projects are likely willing to talk. They are also selling and staffing other projects.
Rising Star
I wouldn't say light-years beyond that. There are a few clustering techniques that are very commonly used, for example.
A major requirement is understanding the limitations of the methods. Anyone can run 2 variable regressions on a dataset. Do you know when you shouldn't? How sparse a matrix before it runs but the results are meaningless? Do you know when sparse is allowed? Can you understand how time series is affected by seasonality? Are you able to recognize a confound?
All to often pretty graphics are generated with either garbage data or garbage analysis. Then you have garbage in Gospel out. People inheritantly trust pretty graphics .
Learn python and tableau
Thanks you both for the advice!! Now all that is left is for me to do the work!