## Data Science and Statistics: different worlds?

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Chris Wiggins (Chief Data Scientist, New York Times)

David Hand (Emeritus Professor of Mathematics, Imperial College)

Francine Bennett (Founder, Mastodon-C)

Patrick Wolfe (Professor of Statistics, UCL / Executive Director, UCL Big Data Institute)

Zoubin Ghahramani (Professor of Machine Learning, University of Cambridge)

Chair: Martin Goodson (Vice-President Data Science, Skimlinks)

Discussant: John Pullinger (UK National Statistician)

In the last few years data science has become an increasingly popular discipline. Often linked to the use and analysis of ‘big data’, data scientists are seen as the new professionals who can unlock the potential of an increasingly data-rich world, and to generate economic and social benefits from the data revolution.

However within the world of statistics, the ‘big data’ and ‘data scientist’ developments are sometimes labelled as hypes, and ‘data science’ is seen as a rebranding of what should be statistics. One of the often heard criticisms of big data analytics is that there’s a lack of statistical rigour which can lead to the wrong decisions.

As with any new discipline there are questions about exactly what data science is. Has the relevance of statistics been diminished because of new types of data or technologies which need a radical new approach? Is data science about ‘getting the job done’, and statistics about the deeper scientific understanding? Are our universities offering students the right skill sets to meet the high demand for data scientists?

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5 years ago and this is still so relevant here in South Korea.

Another intentional youtube algorithm to counter the fraud election officials being exposed..

Absolutely brilliant stuff, with some brilliant minds. Thank you. 5 years on and the debate still stands. It would be great if the same panel got together again in 2021 for a new debate. 🙂

I thoroughly enjoyed this talk, and great learning too. Thank you RSS 😄

Machine Learning is the natural evolution and development of Statistics

We need these series on Netflix right inmediately

23:00 – 24:50 Such an accurate and interesting observation….and so perfectly said.

All subsets of Maths. 🙂

It's true that a data scientist is a better statistician than a computer engineer and better computer engineer than a statistician, BUT, it's worse computer engineer than a computer engineer, and worse statistician than a statistician.

104:32-113:45 An ideal curriculum for a data scientist and staticians..?

Giving this a thumbs up for the statistician!! 👏🏻👍🏻

It is interesting that a data scientist should have a background in both statistics and computer science. Both these fields are rigorous. Therefore, I support an idea that division not only of labour but also knowledge is the most effcient way for the data science field how to evolve. Actually these two should not be brought together, but left seperated.

Do you need exceptional hacking skills to be an awesome Data Scientist?

a lot of heat but really no light, and it's a compliment,as data science is really in it's infancy and light years away from really becomming an exact science like maths or physics,

.At the current state of this science (?) i think it is closer to astrlogy than astrnomy.

And it being based on data as it's raw material it is subject to highly dubious nature of that comodity.

In my opinion Data Science at present can be looked upon at best as sophisticated hypothesis generating device which is utterly contingent on data ,

in other words ,at the end of the day it (data science) is inductive thinking in disguise which humans have been doing since

the inception of humanity.

I enjoyed the comparison between Statistics and Mathematics by senior staff members both from academia and industry.

I really enjoyed the discussion. Yes, statistics is difficult. But it needs to be understood to be used correctly. I'd like to hear what this group says now, currently about the combination of programming and statistics. Data science has been included as a degree at my university, however it is still so difficult to understand how the data scientist actually is defined. However, after 3 years of statistics, when you get to the end of all the theoretical learning and finally get into putting some stuff in R, it is really amazing to see how everything comes together and how modelling can finally be done. It is something truly satisfying. And I think the rigorous statistical background is quite necessary to get there and truly enjoy and understand the experience. I think the true data scientist is someone who enjoys the process and beauty of statistics, as well as the processes and beauty of computer science. Not someone who leans to either side, but rather enjoys putting them together.

big data also involved data , anything anywhere you want statistics will involved so big data is the small part of statistics

Great panel, but dont think they have ever delivered a solution in production working with crazy, demanding and non-mathematical Banking or any other client. They are great in knowing the theories but real world is totally different than theories. To be a good data scientist, you need only 2 things – 1. "I can do it" attitude and 2. Common sense. Nothing else.

Instead of placing all these disciplines into one and calling it data science just create a team of statisticians, analyst, computer scientist and any other field to work on the project at hand

Data Science is more about linear algebra than statistics !

"Big Data" means data volumes too large to handle conveniently with current technology. It's been around for a very long time; it's just the orders of magnitude that change. The question is whether there really is a separate trade of "Data Scientist"? Maybe there are already people who can do what is wanted, just using different names for the job?

From the floor questions, its sounds like the statistics teaching community could take a leaf from the way graduate business schools teach using the case method. Perhaps this might be a new line for HBS?

One of the most interesting discussions about this topic. Insightful!

51:00 Stats is its own discipline or else math funding issues

23:20 UG stats