Leaving aside the practical skills used in industry like coding in python and R, how much theory does a person need to have a job doing statistical analysis. I am a an engineering graduate with only one intro stats course behind my belt. I’d like to move into something more data and statistics oriented but not necessarily machine learning related.
I’d be aiming higher than basic descriptive statistics that business analysts might do.
But I’m not going for any super deep research positions.
I’d like to be able to run experiments and infer cause for useful predictions in industry.
Textbooks I’ve downloaded and would like to start studying are:
Wasserman All of Statistics
McElreath Statistical Rethinking
Nancy Reid The theory of the design of experiments
And I was recommended by a prof:
- Burnham Kenneth Model Selection and Multimodel Inference
If I read these books would I have enough theory to get a job (assuming I also have practical skills by the time I’m done these books)? Are these books not enough? Is there redundant overlap between these books?
Any advice is greatly appreciated, if I have any misconceptions feel free to point them out. Thank you for your time.