This post will describe where statistics falls in terms of similar areas of study, particularly in terms of shared content with other areas, as opposed to the areas that have most theoretical overlap with statistics.
Most similar areas
- Data science: Leans more towards software engineering.
- Business analytics: This is a newer term in my opinion, or at least popular in Australia. Technically leans more towards dashboards and visualisation.
- Machine learning: Probably one of the closest in similarity and practice. I think the main distinction in practice is that statistics is more concerned about explaining why something is happening, while machine learning focusses more on predicting future outcomes. Both can be important.
There are, of course, many other areas that make heavy use or statistics, or are even essentially just statistics in a more specialised setting, e.g. econometrics, biostatistics, epidemiology, actuarial science.
Special mentions go to operations research, which shares stochastic processes, simulation, queueing theory, and decision analysis, but is centred on optimisation rather than inference; and also signal processing and information theory, where shares estimation theory, Kalman filtering, spectral analysis, and entropy-based methods are deeply statistical, just expressed in engineering language.
Applied mathematics also builds up some of the calculus foundations that are useful for statistics, helping to prove foundational results such as the central limit theorem and the law of large numbers.
Data engineering is also an important field, as the process of data integration/pipelines is what converts raw data into data at various stages of cleanliness in which statistical techniques can be applied.
