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And the rise of the data scientist. These pictures speak better than words. They represent keyword popularity according to Google. These numbers and charts are available on Google.

Other public data sources include Indeed and LinkedIn (number of applicants per job ad), though they tend to be more job market related. 

Feel free to add your charts, for keywords such as newsql, map reduce, R, graph database, nosql, predictive analytics, machine learning, statistics etc.

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Comment by Vincent Granville on August 26, 2013 at 3:43pm

@Gary: I checked people and companies advertising statistical consulting services on Google. Half of them offer services to help you write your PhD thesis if you are a graduate student in statistics. This is pretty amazing.

Comment by Vincent Granville on August 26, 2013 at 9:51am

@Bill: I did the long route, PhD in statistics. But I no longer use eigenvalues, GLM and most of what you find in academic textbooks. I also do a substantial amount of data gathering and processing, model-free optimization and yes even with confidence intervals ( based on Analyticbridge's first theorem), lots of algorithms, machine learning, pattern recognition, computer science, computational complexity. And last but not least, I'm a senior expert and business leader with business acumen, business management experience. Thus no, I'm not a statistician anymore.

Also, if you look at the job openings or accredited statisticians listed on the AMSTAT website, they are pretty much all related to the pharmaceutical industry and biostats. That makes me even further different from a statistician.

Comment by Gary D. Miner, Ph.D. on August 26, 2013 at 9:26am

Yes, this is happening ......and the "death of the statistician" started before the approximately 2004 year you have on the X-Axis. I saw it happening starting as early as the late 1990's. When I attended JSM {Joint Statistical Meetings} in TORONTO in year 2000 I listened to the President speak at the Presidential Banquet, starting his talk with something like "....You statisticians, especially the consulting statisticians, are going to have to do an 'about face' if you want to maintain consulting jobs in the years ahead......We are going back / forward to what was the predominant data analytic method prior to the 19th Century.....e.g. Bayesian analyses.....". The company I now work for would give a "door prize" to the CONSULTING STATISTICIANS sub-group of the JSM. I would usually drop by for a few minutes of their "annual Tuesday evening meeting" at JSM; about 2007 I dropped by, just to see a large graph which depicted STATISTICAL CONSULTING JOBS going down at a 45 degree angle since the year 2000; the group was "puzzled' - "What can we do?" - "What is the problem?" Obviously that had not listened to their "president" in the year 2000. What has happened is what I was observing with fewer and fewer statisticians being hired by commercial companies; the decline was what I was predicting also. I had already turned my attention in the late 1990's to "Predictive Analytics" ...... and "Decisioning"..... which currently are being represented by the 'in words': Analytics, Data Scientist, Big Data, Hadoop, Data Analyst, etc. ........I do not believe this trend will go away as the fundamental processes are in operation, only the new "hot terms" will change in the foreseeable future ......

Comment by Bill raynor on August 26, 2013 at 9:02am

Nate Silver at the last JSM: "Data scientist is just a sexed up word for statistician."  Personally, I think the depends on what you include in the set of statisticians, but do agree it is a marketing term for those who never bothered to do the long route. 

Comment by Vincent Granville on August 25, 2013 at 4:13pm

A reader wrote: Most of the data analysts are statisticians. These are just names!

Here's my answer:

Data analyst is a junior title, typically a guy with a BS or BA degree. Statisticians have a more theoretical background, are trained to use models developed before the advent of big data, and have a MS or PhD degree. A guy spending his days writing SQL queries and reports is a data analyst.

A lot has been said about the death of statistics, including by leading statisticians. I believe statistical science will eventually come back, but it will be more applied, adapted to big data, and less model-driven. It will be integrated, together with computer science, predictive modeling, data mining, machine learning, some aspects of operations research and six sigma, database architecture, under a big umbrella, called data science, business analytics, decision science, data intelligence, analytics or some other word yet to be created or re-used. We are currently in the middle of this analytics revolution.

In particular, guys like me, although having a new job title – data scientist – still do statistics part-time, even very theoretical, cutting edge sometimes.  In my case, I am reviving a 250 years old but very robust technique that was deemed too complicated in 1750 due to lack of computing power, and abandoned. The lack of computing power, back in 1750, resulted in new, mathematically-friendly techniques developed later around 1800, with simple formulas, such as least square regression. This framework has survived to this day, and could be the cause of the decline of traditional statisticians today, as robustness is more important than ever with big data, and computational complexity is no longer an issue when gigabytes of data can be processed in a few minutes on distributed systems (cloud with map reduce). The problem is compounded by the fact that most modern scientists, geographers, physicians, econometricians, operations research professionals, engineers etc. all have a very decent, applied statistical knowledge. However, software engineers and computer scientists sometimes ignore or misuse statistical science, sometimes as badly as journalists, with bad consequences, such as development of systems (e.g. recommendation engines) with large numbers of undetected fake reviews and fraud. Eventually, statistical science will start pouring into these communities.

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