Speaking as an applied statistician, there are general methods that apply to any field such as descriptive and inferential statistics, sampling, and core modeling methods such as linear regression and PCA.

Once we get beyond these basics, applied statistics begins to diverge quite a bit. For the work I do, methods developed in biostatistics and epidemiology, econometrics and psychometrics are most useful.

Predictive analytics has been part of applied statistics from the very beginning, so much so that "predictive" was not used until the past decade or so. Statisticians simply called predictive analytics "statistics."

Computer scientists have made important contributions to this corner of applied statistics, and at the academic and practitioner level there is increasing collaboration between the two disciplines.

Though some types of predictive analytics are increasingly automated, statistics is not going to vanish any time soon. It's too complex and innovation is increasingly rapid. Human experts will be needed for the foreseeable future. IMO.

A greatly underappreciated aspect of statistics is that it is also a way of thinking, not merely math and programing. It also opens doors to many other fields, including art, music, literature and philosophy.

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