Recommended Self-Study Path for Statistics

I'm a middle manager at a large tech company that recently took responsibility for a few engineering teams that do some stats heavy work. Each producing forecasts where we talk about accuracy of predictions, some that use machine learning & the like.

I have a fairly shallow understanding. I took a basic stats class in undergrad 20 years ago. Over the years, I’ve seen various analysis so I’m not totally lost when people talk about p-values (but also have a lot of gaps where some of the details are lost on me).

I’d like to strengthen my understanding so I can better understand/appreciate/represent what the teams are doing. Any recommendations on a course of study?

FWIW, I’ve considered just buying a text book or hiring a tutor. I looked at classes at the local community colleges, but both the syllabus suggests a fairly slow pace (and most things I feel comfortable with) and when I took a previous class (iOS development) it was so dang slow in pacing.

Get your own consigliere. Hire a consultant to advise/mentor you individually.

You might find one on Hourly Nerd > https://hourlynerd.com/your-matches/information-technology-a...

These books are more for casual reading, except for Book 3. If I may, do you have more recommendations similar to Book 3? In addition to my personal interest, my read of the OP request was he looking for more technical details. For myself, what books are good for a second or third course in stats? I have a finance background, so I'm familiar with the intro stuff in Book 3.

As a recommendation to the OP, "Collective Intelligence" by Toby Segaran is amazing.

I encounter this a lot at work. People needing more advanced stats skills for a new role and not having much training in it or if they did it was years ago. (I work in finance which has become increasingly quant and stats heavy - faster than training in it has).

My advice: Figure out exactly what type of stats work your teams are doing. Make a list of those topics. Random example: are those Kolmogorov–Smirnov or Mann–Whitney tests? Then hire a tutor who knows that stuff - maybe a grad student somewhere, can be remote over skype even.

If you are not 100% sure what you are looking at at work and what to put on this list of topics...hire a tutor and show them stuff from work (if the work is proprietary/confidential, recreate it with dummy data or just give rough examples) and ask what topics would be needed to nail one's understanding of this work.

Statistics is a huge subject and if you buy a textbook you may spend a ton of time on stuff that's just not relevant when you could be going a bit deeper into a sub-topic that is very relevant to your work. Also a lot of what looks like statistics is actually found under applied math books/courses not statistics.

Lastly, in case this needs be said, after you get the basics on a stats topic, the most important question to ask a stats tutor is "where do people usually fuck up when doing this?"

Stats in practice is often more about not making errors than it is about accuracy. Find out where people often fuck it up, especially as a manager and 2x as they are not statisticians either it sounds like.

I agree with the above recommendation.

I have read John Cook's blog for some time and think he might be a good fit as a consultant for bringing you up to speed and reviewing your current teams' work.

http://www.johndcook.com/blog/

I suspect it would be worth your time to get a consultant like him to come in for a week, review all the work going on in your org, and maybe lay out a learning plan or crash course in developing a safety net for you.