Analytics is beating statistics
icrunchdata, which is a data-related jobs site, has introduced what it is calling the Big Data Jobs Index
If you believe the numbers, it looks as though analytics is way ahead in the synonym game, followed by data science, but at least statistics is still ahead of business intelligence. And at least this is a bar chart, though not an index in any usual sense of the term.
The company describes Big Data as having ‘fueled one of the most hyper-growth niches of employment in a century’, but since their projection is for the sector to grow to nearly 1% of the US job market by 2015, we clearly need to be careful of the definition of fast growth
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »
Statistics – Analytics – Data Science – What’s the difference?
12 years ago
The main difference is which buzzword is in the advert, I think.
12 years ago
I found it rather amusing to be in the company of 3 professionals over lunch – a data scientist, a risk analyst, and an actuary – and listen to each of them argue that their profession was the supposed “sexiest job” of the 21st century (coined by Hal Varian), and was more sophisticated than the other. As an innocent bystander, I felt both humbled and amused at the same time. I was tempted to stop them mid-conversation as just blurt out – at the end of the day, aren’t you all just applied mathematicians?
12 years ago
There is definitely a sense in which we’re all applied mathematicians, but in fact the people called ‘applied mathematicians’ do very different things. Even ‘applied probabilists’, like my colleague Ilze Ziedins (who are card-carrying applied mathematicians but often live in statistics departments) don’t overlap that much with statisticians/data scientists/analysts, though they do overlap with actuaries. And mathematical statisticians, even those with no real interest in applications, do quite different types of research from pure mathematicians and theoretical probabilists, and publish very different sorts of papers in different journals. Having statistics and mathematics in the same university department tends to work out badly, in a way that having theoretical and applied statisticians in the same department or pure and applied mathematicians in the same department doesn’t.
By contrast, statistics/data science/analytics are much more closely related, with ‘data science’ perhaps tending to indicate a need for more computational skill and ‘analytics’ maybe suggesting a role as speaker-to-managers. My (slightly prejudiced) view is that ‘data science’ and ‘business analytics’ are effectively just euphemisms for ‘statistics’.
12 years ago
That’s interesting. What’s your opinion on the ‘intellectual superiority’ front? The actuary and data scientist couldn’t stop arguing about whose job was more intellectually challenging and had a higher barrier to entry. (Interestingly the risk analyst was quiet during this part of the conversation)
12 years ago
I think actuaries have a higher barrier to entry — their certification process is quite strict, but anyone can call themselves a data scientist.
Right now, data science jobs are probably less likely to be routine than the other labels, but that could easily change.
12 years ago
Would you have an idea of how the Masters program (or perhaps even PhD) in statistics at Auckland compares to the Actuarial exams, in terms of difficulty?
12 years ago
I’d say the MSc program is easier than the actuarial exams. For the PhD program the comparison isn’t really meaningful — what’s hard about the PhD is independent research, not learning specific things.
[The full set of actuarial exams is also quite expensive.]
12 years ago
‘data science’ and ‘business analytics’ are effectively just euphemisms for ‘statistics’
Here’s my take – based on the tools people use:
statistics – R, SAS
business intelligence – SAP, Cognos
data science – Hadoop, Mapreduce
The difference between analytics and analysis is that the former subsumes the latter. Analytics designs the system, decides what tools to use for analysis and connects everything together – methodology. Analysis studies the numbers – crunchodology.
All the roles have ‘statistics’ in common – in the same way that many sports have the word ‘bat’
12 years ago