What is Reddit's opinion of Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python?

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For ML I would recommend practical statistics for data scientists as a starting point and just generally good to have in ur toolbox. It covers everything from basic stats to supervised and unsupervised methods and implementation in both R and Python.

For NLP personally I’ve used a mix of online resources to fit my use cases. I lean on python more than R for NLP though but if u can learn R u can learn python implementations (Jupyter notebooks are the same as R markdown). NLTK is one library you can look at for sentiment analysis and for things like topic modeling you can look into LDA (latent dirichlet allocation - not linear discriminant analysis). Word clouds are a pretty simple implementation as well. There are plenty of tutorials for all of these you can find online.

Also, from experience (and one reason I recommend that book) many ML courses and books get into ML topics that wouldn’t be relevant to IO work (e.g., image classification, computer vision) so make sure you review the material to be sure it’s relevant. It’s easy to go down rabbit holes of ML knowledge you won’t really need (at least at this level).

Good luck!

 

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