I'm a working data scientist with solid experience in regression, other machine learning type algorithms, and programming (both for data analysis and general software development). Most of my working life has been focused on building models for predictive accuracy (working under various business constraints), and building data pipelines to support my own (And others) work.

I have no formal training in statistics, my university education focused on pure mathematics. As such have missed out on learning many of the classical topics, especially the various popular hypothesis tests and inferential techniques.

Are there any references for these topics that would be appropriate for someone with my background and level of experience? I can handle (and appreciate) mathematical rigour, and also enjoy algorithmic perspectives. I tend to like approaches that offer the reader guided exercises, with both a mathematical and programming focus.

I'm a working data scientist with solid experience in regression, other machine learning type algorithms, and programming (both for data analysis and general software development). Most of my working life has been focused on building models for predictive accuracy (working under various business constraints), and building data pipelines to support my own (and other's) work.

I have no formal training in statistics, my university education focused on pure mathematics. As such have missed out on learning many of the classical topics, especially the various popular hypothesis tests and inferential techniques.

Are there any references for these topics that would be appropriate for someone with my background and level of experience? I can handle (and appreciate) mathematical rigour, and also enjoy algorithmic perspectives. I tend to like references that offer the reader guided exercises, with both (or either) a mathematical and (or) programming focus.