# “The sexy job in the next ten years will be statisticians”

I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?

–Hal Varian [Google’s chief economist and author of your textbook], The McKinsey Quarterly, January 2009

McKinsey seems to have taken this point to heart, and they’ve got a new report titled “Big Data: The Next Frontier for Innovation, Competition and Productivity”, profiled here by the New York Times.

…the study also identifies challenges. One hurdle is a talent and skills gap. The United States alone, McKinsey projects, will need 140,000 to 190,000 more people with “deep analytical” skills, typically experts in statistical methods and data-analysis technologies.

McKinsey says the nation will also need 1.5 million more data-literate managers, whether retrained or hired. The report points to the need for a sweeping change in business to adapt a new way of managing and making decisions that relies more on data analysis. Managers, according to the McKinsey researchers, must grasp the principles of data analytics and be able to ask the right questions.

This should make you feel better about studying QM. If the MSc teaches you anything, it’s how to think quantitatively, and that’s a skill that will be increasingly valuable in the years to come. To quote Hal Varian again (thinking like an economist and a statistician):

now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

Concerning…”If the MSc teaches you anything, it’s how to think quantitatively” …I think one of the main improvements regarding the QM part would be to make notation more consistent throughout the course. It is quite annoying to get 3 versions of ML notation/derivation, 2 of GMM (where one uses a q for something that is called a J statistic) and so on and so fourth. It is inefficient because we dont learn anything by adapting to a new notation. (Apart from other questions like…Is it sensible to have a QM2 exam worth .25*1/18*100 %)? I know that most readers are not responsible for this, but they might have some impact on the development of the SGPE in the future.

I agree completely, and I will try to convince the powers that be…

I agree it is useful to think about the world quantitatively, but this must be done with a critical mind. Data are useful, but not when bluntly manipulated towards creating convenient truths.

I would therefore recommend to read (after the exam session), Charles Seife’s “Proofiness: The Dark Arts of Mathematical Deception”

http://well.blogs.nytimes.com/2010/10/29/the-dark-art-of-statistical-deception/

Finally, I would add as an example, that the financial crisis of 2008 has shown a purely quantitative articulation of markets is plainly insufficient.

The really sexy future statisticians will be those who also demonstrate interpretive savvy.

best

Des

I love sex