According to Wikipedia, Econometrics is the application of mathematics, statistical methods, and computer science to economic data and is described as the branch of economics that aims to give empirical content to economic relations. It sifts through mountains of data to extract simple relationships. Generally for positions like Analytic Consultant, Data Scientist, Statistician, Quantitative Analyst, background in maths, stats or econometrics is required. What separates econometricians in terms of what they learn in school and how they apply acquired knowledge in work? Or do these fields overlap so much that there is not much to distinguish?
An interesting question that leads to a debate among econometricians. Some consider that
Econometrics is just statistics applied to economic problems
Econometrics is just statistics applied to economic problems—nothing more and nothing less. We should probably call it “statistical economics,” but I guess people feel that the term “econometrics” has a better ring to it. The only cost of using the term “econometrics” is that we are sometimes fooled into thinking that we work on a distinct discipline, separate from statistics. This is not true.
From John Stachurski at ANU (see his notes p.108 in Econometric Theory freely downloadable)
Others consider that
Eonometrics by no means the same as economic statistics
The key idea is that economic theory is crucial to understand measurement. Neither theory nor measurement on its own is sufficient to further our understanding of economic phenomena. We need both and measurement without theory is unlikely to provide a satisfactory explanation of the way economic forces interact with each other.
Another difference is, as economists, we are biased towards establishing causal relationships. So, this explains our focus on endogeneity issues and strategies of identification.
The following quote might also be useful. The following is quoted from Mostly Harmless Econometrics, by Angrist and Pischke
Two things distinguish the discipline of Econometrics from our older sister field of Statistics. One is a lack of shyness about causality. Causal inference has always been the name of the game in applied econometrics. Statistician Paul Holland (1986) cautions that there can be no causation without manipulation, a maxim that would seem to rule out causal inference from non-experimental data. Less thoughtful observers fall back on the truism that correlation is not causality. Like most people who work with data for a living, we believe that correlation can sometimes provide pretty good evidence of a causal relation, even when the variable of interest has not been manipulated by a researcher or experimenter.
The second thing that distinguishes us from most statisticians and indeed most other social scientists is an arsenal of statistical tools that grew out of early econometric research on the problem of how to estimate the parameters in a system of linear simultaneous equations. The most powerful weapon in this arsenal is the method of Instrumental Variables (IV), the subject of this chapter. As it turns out, IV does more than allow us to consistently estimate the parameters in a system of simultaneous equations, though it allows us to do that as well.
However, this is not to say that causal inference belongs only to econometrics. Wasserman, in All of Statistics, remarks at how parallel developments were made within statistics and econometrics,
The use of potential outcomes to clarify causation is due mainly to Jerzy Neyman and Don Rubin. Later developments are due to Jamie Robins, Paul Rosenbaum and others. A parallel devel- opment took place in econometrics by various people including Jim Heckman and Charles Manski.