How to know the state-of-the-art recommended approaches for data science?
A few comments:

There is no scientific domain of &quot;data science&quot;, instead there are multiple fields which are related to data science: statistics, ML, NLP, computer vision, signal processing... and a lot of other fields which overlap and/or focus on specific applications, for instance bioinformatics. All of these domains are highly active and specialized, so it would just be impossible to monitor every possible advance.
There is no unique recommended way: first, people disagree all the time about the best way to do X. Second, it's very rare that a method would become completely obsolete. For example TFIDF still makes sense in many use cases, with low-resource languages or when there are efficiency constraints for instance.
In order to comprehensively follow the state of the art, one would have to follow the research publications. At best it's doable for a specific domain, for example one can more or less get an idea of what happens in NLP by browsing through the main conferences. A more realistic option is to wait for the advances to reach the mainstream professionals, for example by browsing regularly through DataScienceSE and/or CrossValidated.
Final suggestion: old books are very useful to fully understand why/how things are done a certain way. We often see errors here on DataScienceSE which are due to people trying to apply methods without understanding them.