Frankly, I don't think the law of large numbers has a huge role in industry. It is helpful to understand the asymptotic justifications of the common procedures, such as maximum likelihood estimates and tests (including the omniimportant GLMs and logistic regression, in particular), the bootstrap, but these are distributional issues rather than probability of hitting a bad sample issues.
Beyond the topics already mentioned (GLM, inference, bootstrap), the most common statistical model is linear regression, so a thorough understanding of the linear model is a must. You may never run ANOVA in your industry life, but if you don't understand it, you should not be called a statistician.
There are different kinds of industries. In pharma, you cannot make a living without randomized trials and logistic regression. In survey statistics, you cannot make a living without Horvitz-Thompson estimator and non-response adjustments. In computer science related statistics, you cannot make a living without statistical learning and data mining. In public policy think tanks (and, increasingly, education statistics), you cannot make a living without causality and treatment effect estimators (which, increasingly, involve randomized trials). In marketing research, you need to have a mix of economics background with psychometric measurement theory (and you can learn neither of them in a typical statistics department offerings). Industrial statistics operates with its own peculiar six sigma paradigms which are but remotely connected to mainstream statistics; a stronger bond can be found in design of experiments material. Wall Street material would be financial econometrics, all the way up to stochastic calculus. These are VERY disparate skills, and the term "industry" is even more poorly defined than "academia". I don't think anybody can claim to know more than two or three of the above at the same time.
The top skills, however, that would be universally required in "industry" (whatever that may mean for you) would be time management, project management, and communication with less statistically-savvy clients. So if you want to prepare yourself for industry placement, take classes in business school on these topics.
UPDATE: The original post was written in February 2012; these days (March 2014), you probably should call yourself "a data scientist" rather than "a statistician" to find a hot job in industry... and better learn some Hadoop to follow with that self-proclamation.