Data science, AI, NLP, and visualization are changing so fast. I wonder if there is a way/blog that shares the latest updates and recommended approach using certain techniques or avoid using others. For example, many NLP books are old and they would provide examples using TF-IDF. However, nowadays there are much better approaches but they are also changing fast. I am hoping to find some source that would say use these new techniques and avoid using those old techniques. Searching the web can help, but will bring back a lot of noise.
A few comments:
- There is no scientific domain of "data science", 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.
data science is sometimes considered applied machine learning so maybe you want to have a look at this to get an idea of what people are doing in different parts of the field https://paperswithcode.com/search?q_meta=&q_type=&q=data+science
This has a good algo that selects papers that is considered by researchers at meta aka facebook ai to be good for sota here is the github on the algo https://github.com/paperswithcode/sota-extractor#automatic-sota-state-of-the-art-extraction
And a bit on the about page "dataset_citations" - zero or more Link objects, representing the papers that are the primary citations for the dataset sota - the Sota object representing the state-of-the-art table on this dataset"
The state of the art will evolve and as long as this project is maintained it seems like a good place to check for the next 3 years. Citations are the main criteria here but what people use on the job is years behind.