Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 13, 2014, at the `SMi Big Data in Pharma' conference in London, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's Introductory View on Big Data and Data Science (Version 7)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 12, 2015, at the 'SAS Forum Switzerland' in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's introductory view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 8)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on July 2, 2015, at 'Swiss Re' in Adliswil, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 5)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 21, 2014, at the 'Research Seminar in Statistics' of the University of Geneva in Geneva, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's View on Big Data and Data Science in Pharmaceutical Developm...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 13, 2014, at `F. Hoffmann-La Roche' in Basel, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as the pharmaceutical industry. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Swiss Statistician's 'Big Tent' View on Big Data and Data Science (Version 10)Prof. Dr. Diego Kuonen
Keynote talk given by Dr. Diego Kuonen, CStat PStat CSci, on October 21, 2015, at the `Austrian Statistics Days 2015' in Vienna, Austria.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on August 26, 2014, at the `Zurich Machine Learning and Data Science' meetup in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Overview of Big Data, Data Science and Statistics, along with Digitalisation,...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 29, 2016, at the `University of Applied Sciences of Western Switzerland' (`Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud', HEIG-VD) in Yverdon-les-Bains, Switzerland.
A Swiss Statistician's 'Big Tent' Overview of Big Data and Data Science in Ph...Prof. Dr. Diego Kuonen
'President's Invited Speaker' keynote talk given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on August 22, 2016, at the '37th Annual Conference of the International Society for Clinical Biostatistics (ISCB)' in Birmingham, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as pharmaceutical development. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' overview of these terms in pharmaceutical development, illustrates the connection between data science and statistics - the terms surrounding the 'sexiest job of the 21st century' - and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's Introductory View on Big Data and Data Science (Version 7)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 12, 2015, at the 'SAS Forum Switzerland' in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's introductory view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 8)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on July 2, 2015, at 'Swiss Re' in Adliswil, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 5)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 21, 2014, at the 'Research Seminar in Statistics' of the University of Geneva in Geneva, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's View on Big Data and Data Science in Pharmaceutical Developm...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 13, 2014, at `F. Hoffmann-La Roche' in Basel, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as the pharmaceutical industry. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Swiss Statistician's 'Big Tent' View on Big Data and Data Science (Version 10)Prof. Dr. Diego Kuonen
Keynote talk given by Dr. Diego Kuonen, CStat PStat CSci, on October 21, 2015, at the `Austrian Statistics Days 2015' in Vienna, Austria.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on August 26, 2014, at the `Zurich Machine Learning and Data Science' meetup in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Overview of Big Data, Data Science and Statistics, along with Digitalisation,...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 29, 2016, at the `University of Applied Sciences of Western Switzerland' (`Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud', HEIG-VD) in Yverdon-les-Bains, Switzerland.
A Swiss Statistician's 'Big Tent' Overview of Big Data and Data Science in Ph...Prof. Dr. Diego Kuonen
'President's Invited Speaker' keynote talk given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on August 22, 2016, at the '37th Annual Conference of the International Society for Clinical Biostatistics (ISCB)' in Birmingham, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as pharmaceutical development. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' overview of these terms in pharmaceutical development, illustrates the connection between data science and statistics - the terms surrounding the 'sexiest job of the 21st century' - and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data, Data-Driven Decision Making and Statistics Towards Data-Informed Po...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 20, 2015, at the Swiss Statistical Society's celebration of the `World Statistics Day 2015' in Olten, Switzerland.
Further information are available at https://worldstatisticsday.org/blog.html?c=CHE
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Demystifying Big Data, Data Science and Statistics, along with Machine Intell...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 25, 2016, at the `Statistics at Nestlé in Switzerland' event of `Nestlé' in Vevey, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 6)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on April 23, 2015, at the 'ZüKoSt: Seminar on Applied Statistics' of the ETH Zurich in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 9)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 1, 2015, at the `Joint SCITAS and Statistics Seminar' of the EPFL in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 13, 2017, at the `Information Builders Think Tank Lunch' in Zurich, Switzerland.
The Power of Data Insights - Big Data as the Fuel and Analytics as the Engine...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 1, 2017, at the `Microsoft Vision Days - Intelligent Cloud' event of Microsoft Switzerland in Wallisellen, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's `Big Tent' View on Big Data and Data Science in Health Scien...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on April 18, 2016, at the `Nestlé Institute of Health Sciences' in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as health sciences. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' view on these terms in health sciences, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Data as Fuel and Analytics as Engine of the Digital Transformation: Demystic...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on September 19, 2017, at the "ITU-Academia Partnership Meeting: Developing Skills for the Digital Era" in Budapest, Hungary.
See https://www.itu.int/en/ITU-D/Capacity-Building/Pages/events/academia2017.aspx
Big Data, Data Science, Machine Intelligence and Learning: Demystification, T...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 14, 2017 at Eurostat's international conference `New Techniques and Technologies for Statistics (NTTS) 2017' in Brussels, Belgium.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Glocalised Smart Statistics and Analytics of Things: Core Challenges and Key ...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 18, 2017 within Eurostat's special topic session `STS021: From Big Data to Smart Statistics' at the `61st ISI World Statistics Congress' (ISI2017) in Marrakech, Morocco.
Big Data as the Fuel and Visual Analytics as the Engine Mount of the Digital ...Prof. Dr. Diego Kuonen
Public keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 7, 2017, in the context of the `CAS Data Visualization' of the `Bern University of the Arts' (HKB) in Berne, Switzerland.
See https://www.hkb.bfh.ch/de/weiterbildung/design/cas-data-visualization and http://bka.ch/worte/rubriken/worte/kein-datensalat
(Big) Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 15, 2018, within the TIBCO webinar entitled "Demystifying the Hype: [Big] Data as Fuel and Analytics as the Engine of Digital Transformation"; see
https://www.tibco.com/events/demystifying-hype-big-data-fuel-and-analytics-engine-digital-transformation
---------------
ABSTRACT
---------------
The digital revolution is truly underway: terms such as big data, cloud, internet of things, internet of everything, the fourth industrial revolution, smart cities and data economy are no longer just concepts - they are changing our lives in new and exciting ways.
Digital Transformation started with a first wave of digitalisation, which resulted in the (big) data revolution. But now a second wave of digitalisation is needed to enable learning from (big) data and to generate increased value for both business and society as a whole.
This presentation discusses how analytics, the science of "learning from data" or of "making sense out of data", becomes the engine of a new wave of Digital Transformation, and illustrates that the biggest challenge therein is the veracity of the "data pedigree", i.e. the trustworthiness of the data, including the reliability, capability, validity, and related quality of the data.
This presentation looks at demystifying concepts and terms surrounding Digital Transformation and big data. Along with machine intelligence and learning, the connection between data science and statistics is illustrated, and trends, challenges, opportunities, and the related digital skills and principles needed to succeed at Digital Transformation are highlighted.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data, Data Science, Machine Intelligence and Learning: Demystification, C...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 28, 2019 at the "Swiss Cyber Security Days 2019" on February 27-28, 2019 in Fribourg, Switzerland; see https://swisscybersecuritydays.ch/.
"Data as the Fuel and Analytics as the Engine of the Digital Transformation -...Prof. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 14, 2019, within the StatSoft webinar entitled "Data as the Fuel and Analytics as the Engine of the Digital Transformation - Demystication, Challenges, Opportunities and
Principles for Success"; see https://www.statsoft.de/en/dates/webinars/
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 26, 2018 at TIBCO's "Data Innovation Event" in Zurich, Switzerland; see https://www.tibco.com/events/tibco-data-innovation-event
Production Processes of Official Statistics & Data Innovation Processes Augme...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 15, 2018 at the conference "Big Data for European Statistics (BDES)" in Sofia, Bulgaria; see
https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/BDES_2018
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 7, 2018 at the "5th Swiss Conference on Data Science (SDS|2018)" in Berne, Switzerland; see https://sds2018.ch/.
These slides give a data science view of my MIT Ph.D. Thesis work, including a description of the data, the preprocessing/imaging methods, and the interpretation of the resulting seismic images in terms of subduction zone structure. The data preprocessing employs a novel unsupervised machine learning pipeline that computes high accuracy (and precision) receiver functions by iteratively applying multichannel cross-correlation, principal component analysis, and frequency-domain deconvolution.
Big Data, Data-Driven Decision Making and Statistics Towards Data-Informed Po...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 20, 2015, at the Swiss Statistical Society's celebration of the `World Statistics Day 2015' in Olten, Switzerland.
Further information are available at https://worldstatisticsday.org/blog.html?c=CHE
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Demystifying Big Data, Data Science and Statistics, along with Machine Intell...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 25, 2016, at the `Statistics at Nestlé in Switzerland' event of `Nestlé' in Vevey, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 6)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on April 23, 2015, at the 'ZüKoSt: Seminar on Applied Statistics' of the ETH Zurich in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 9)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 1, 2015, at the `Joint SCITAS and Statistics Seminar' of the EPFL in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 13, 2017, at the `Information Builders Think Tank Lunch' in Zurich, Switzerland.
The Power of Data Insights - Big Data as the Fuel and Analytics as the Engine...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 1, 2017, at the `Microsoft Vision Days - Intelligent Cloud' event of Microsoft Switzerland in Wallisellen, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's `Big Tent' View on Big Data and Data Science in Health Scien...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on April 18, 2016, at the `Nestlé Institute of Health Sciences' in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as health sciences. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms 'big data' and 'data science'. This presentation gives a professional Swiss statistician's 'big tent' view on these terms in health sciences, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Data as Fuel and Analytics as Engine of the Digital Transformation: Demystic...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on September 19, 2017, at the "ITU-Academia Partnership Meeting: Developing Skills for the Digital Era" in Budapest, Hungary.
See https://www.itu.int/en/ITU-D/Capacity-Building/Pages/events/academia2017.aspx
Big Data, Data Science, Machine Intelligence and Learning: Demystification, T...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 14, 2017 at Eurostat's international conference `New Techniques and Technologies for Statistics (NTTS) 2017' in Brussels, Belgium.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Glocalised Smart Statistics and Analytics of Things: Core Challenges and Key ...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 18, 2017 within Eurostat's special topic session `STS021: From Big Data to Smart Statistics' at the `61st ISI World Statistics Congress' (ISI2017) in Marrakech, Morocco.
Big Data as the Fuel and Visual Analytics as the Engine Mount of the Digital ...Prof. Dr. Diego Kuonen
Public keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 7, 2017, in the context of the `CAS Data Visualization' of the `Bern University of the Arts' (HKB) in Berne, Switzerland.
See https://www.hkb.bfh.ch/de/weiterbildung/design/cas-data-visualization and http://bka.ch/worte/rubriken/worte/kein-datensalat
(Big) Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 15, 2018, within the TIBCO webinar entitled "Demystifying the Hype: [Big] Data as Fuel and Analytics as the Engine of Digital Transformation"; see
https://www.tibco.com/events/demystifying-hype-big-data-fuel-and-analytics-engine-digital-transformation
---------------
ABSTRACT
---------------
The digital revolution is truly underway: terms such as big data, cloud, internet of things, internet of everything, the fourth industrial revolution, smart cities and data economy are no longer just concepts - they are changing our lives in new and exciting ways.
Digital Transformation started with a first wave of digitalisation, which resulted in the (big) data revolution. But now a second wave of digitalisation is needed to enable learning from (big) data and to generate increased value for both business and society as a whole.
This presentation discusses how analytics, the science of "learning from data" or of "making sense out of data", becomes the engine of a new wave of Digital Transformation, and illustrates that the biggest challenge therein is the veracity of the "data pedigree", i.e. the trustworthiness of the data, including the reliability, capability, validity, and related quality of the data.
This presentation looks at demystifying concepts and terms surrounding Digital Transformation and big data. Along with machine intelligence and learning, the connection between data science and statistics is illustrated, and trends, challenges, opportunities, and the related digital skills and principles needed to succeed at Digital Transformation are highlighted.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data, Data Science, Machine Intelligence and Learning: Demystification, C...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 28, 2019 at the "Swiss Cyber Security Days 2019" on February 27-28, 2019 in Fribourg, Switzerland; see https://swisscybersecuritydays.ch/.
"Data as the Fuel and Analytics as the Engine of the Digital Transformation -...Prof. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 14, 2019, within the StatSoft webinar entitled "Data as the Fuel and Analytics as the Engine of the Digital Transformation - Demystication, Challenges, Opportunities and
Principles for Success"; see https://www.statsoft.de/en/dates/webinars/
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 26, 2018 at TIBCO's "Data Innovation Event" in Zurich, Switzerland; see https://www.tibco.com/events/tibco-data-innovation-event
Production Processes of Official Statistics & Data Innovation Processes Augme...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 15, 2018 at the conference "Big Data for European Statistics (BDES)" in Sofia, Bulgaria; see
https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/BDES_2018
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 7, 2018 at the "5th Swiss Conference on Data Science (SDS|2018)" in Berne, Switzerland; see https://sds2018.ch/.
These slides give a data science view of my MIT Ph.D. Thesis work, including a description of the data, the preprocessing/imaging methods, and the interpretation of the resulting seismic images in terms of subduction zone structure. The data preprocessing employs a novel unsupervised machine learning pipeline that computes high accuracy (and precision) receiver functions by iteratively applying multichannel cross-correlation, principal component analysis, and frequency-domain deconvolution.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
Azure Stream Analytics (ASA) is an Azure Service that enables real-time insights over streaming data from devices, sensors, infrastructure, and applications. In this presentation, we provide introduction to the service, common use cases, example customer scenarios, business benefits, and demo how to get started. We will quickly build a simple real time analytic application that uses an IoT device to ingest data (Event Hubs), process and analyze data (Stream Analytics) and visualize data (PowerBI).
Cortana Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. It is comprised of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. This presentation will cover all the products involved, how they work together, and use cases.
Today, an estimated 8.5% of the Belgian employees are at risk of burnout. This represents a huge potential cost to organizations and a huge personal risk for employees. Together with SD Worx, a large Belgian HR service provider, we succeeded in predicting expected future absenteeism, by combining historical absenteeism with softer measures like evaluations and surveys. At DISummit 2017, we shared the main conclusions of this exciting project, and we offer our view on why we succeeded in this challenge.
Myths and Mathemagical Superpowers of Data ScientistsDavid Pittman
Some people think data scientists are mythical beings, like unicorns, or they are some sort of nouveau fad that will quickly fade. Not true, says IBM big data evangelist James Kobielus. In this engaging presentation, with artwork created by Angela Tuminello, Kobielus debunks 10 myths about data scientists and their role in analytics and big data. You might also want to read the full blog by Kobielus that spawned this presentation: "Data Scientists: Myths and Mathemagical Superpowers" - http://ibm.co/PqF7Jn
For more information, visit http://www.ibmbigdatahub.com
Keynote on why statisticians should also be online marketers.
Read the full blog post here: http://crocstar.com/blog/2017/sorry-statisticians-but-marketing-is-your-job-too
The Age of Data Driven Science and Engineering Persontyle
Slide deck of Ali Syed's talk on 13th October 2015 at Center for Interdisciplinary Mathematics, Uppsala University. Why we all need to rethink and re-imagine how we define and perceive academic disciplines, emergence of data driven sciences and engineering,
explore why talent is the most important ingredient, how the European Data Science Academy (EDSA)is addressing the skills shortage and mass-scale upskilling challenge.
Building better healthcare products with AITom Winstanley
Beyond the hype of AI in health - what does it take to deliver quality products that make a difference and the #TripleWin of academic, healthcare and industrial collaborations
ODI Node Vienna: Best Practise Beispiele für: Open Innovation mittels Open DataMartin Kaltenböck
Vortrag im Rahmen des Data Pioneers Workshop am 10.10.2016 am BMVIT zum Thema Open Innovation und Open Data (Open Innovation mittels Open Data) seitens Elmar Kiesling (TU Wien) und Martin Kaltenböck (SWC) für den ODI (Open Data Institute) Node Vienna.
Can You Really Make Best Use of Big Data?R A Akerkar
How big is big? What are the precise criteria for a data set to be considered big data? At least three major factors that contribute to the bigness of big data: Ubiquity and variety of data capturing devices for different types of information
Increase data resolution. Super-linear scaling of data production rate with data producers. Although big data has other dimensions too but these are not inherent to the "bigness" of big data.
The Limitless Possibilities in Data Science.pdfUSDSI
The big gamut of the data science opportunities beckons you! Yield big data science career goals with the best data science certifications and skills on offer.
Similar to A Statistician's View on Big Data and Data Science (Version 2) (12)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
A Statistician's View on Big Data and Data Science (Version 2)
1. A Statistician’s View on Big
Data and Data Science
Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Statistical Consulting + Data Analysis + Data Mining Services
Morgenstrasse 129, 3018 Berne, Switzerland
www.statoo.info
‘SMi Big Data in Pharma’, London, United Kingdom — May 12 & 13, 2014
2. Abstract
There is no question that big data have hit the business, gov-
ernment and scientific sectors. The demand for skills in data
science is unprecedented in sectors where value, competitiveness
and efficiency are driven by data. However, there is plenty of
misleading hype around the terms ‘big data’ and ‘data science’.
This presentation gives a professional statistician’s view on these
terms, illustrates the connection between data science and statis-
tics, and highlights some challenges and opportunities from a
statistical perspective.
3. About myself
• PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne,
Switzerland.
• MSc in Mathematics, EPFL, Lausanne, Switzerland.
• CStat (‘Chartered Statistician’), Royal Statistical Society, United Kingdom.
• PStat (‘Accredited Professional Statistician’), American Statistical Association,
United States of America.
• CSci (‘Chartered Scientist’), Science Council, United Kingdom.
• Elected Member, International Statistical Institute, Netherlands.
• CEO, Statoo Consulting, Switzerland.
• Lecturer in Statistics, Geneva School of Economics and Management, University
of Geneva, Switzerland.
• President of the Swiss Statistical Society.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
2
4. About Statoo Consulting
• Founded Statoo Consulting in 2001.
• Statoo Consulting is a software-vendor independent Swiss consulting firm spe-
cialised in statistical consulting and training, data analysis and data mining ser-
vices.
• Statoo Consulting offers consulting and training in statistical thinking, statistics
and data mining in English, French and German.
Are you drowning in uncertainty and starving for knowledge?
Have you ever been Statooed?
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
3
5. Who has already been Statooed?
• Statoo Consulting’s clients include Swiss and international companies like
ABB;
Alcan Aluminium Valais;
Alstom;
Arkema, France;
AstraZeneca, United Kingdom;
Barry Callebaut, Belgium;
Bayer Consumer Care;
Berna Biotech;
BMW, Germany;
Bosch, Germany;
Cargill;
Cr´edit Lyonnais, United Kingdom;
CSL Behring;
F. Hoffmann-La Roche;
GfK Telecontrol;
GlaxoSmithKline, United Kingdom;
H. Lundbeck, Denmark;
Lombard Odier Darier Hentsch;
Lonza;
McNeil, Sweden;
Merck Serono;
Mobiliar;
Nagra Kudelski Group;
Nestl´e Frisco Findus;
Nestl´e Research Center;
Novartis Pharma;
Novelis;
Novo Nordisk;
Pfizer, United Kingdom;
Philip Morris International;
Pioneer Hi-Bred, Germany;
PostFinance;
Procter & Gamble Manufacturing, Germany;
publisuisse;
Rodenstock, Germany;
Sanofi-Aventis, France;
Saudi Arabian Oil Company, Saudi Arabia;
Smith & Nephew Orthopaedics;
Swisscom Innovations;
Swissmedic;
The Baloise Insurance Company;
tl — Public Transport for the Lausanne Region;
Wacker Chemie, Germany;
upc cablecom;
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
4
6. as well as Swiss and international government agencies, nonprofit organisa-
tions, offices, research institutes and universities like
King Saud University, Saudi Arabia;
Lausanne Hotel School;
Institute for International Research, Dubai, United Arab Emirates;
Paul Scherrer Institute (PSI);
Statistical Office of the City of Berne;
Swiss Armed Forces;
Swiss Federal Institute for Forest, Snow and Landscape Research;
Swiss Federal Institutes of Technology Lausanne and Zurich;
Swiss Federal Office for Migration;
Swiss Federal Research Stations Agroscope Changins–W¨adenswil and Reckenholz–
T¨anikon;
Swiss Federal Statistical Office;
Swiss Institute of Bioinformatics;
Swiss State Secretariat for Economic Affairs (SECO);
The Gold Standard Foundation;
Unit of Education Research of the Department of Education, Canton of Geneva;
Universities of Applied Sciences of Northwestern Switzerland, Technology Buchs NTB
and Western Switzerland;
Universities of Berne, Fribourg, Geneva, Lausanne, Neuchatel and Zurich.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
5
7. Contents
Contents 6
1. Demystifying the ‘big data’ hype 8
2. Demystifying the ‘data science’ hype 15
3. What distinguishes data science from statistics? 34
4. Outlook, challenges and opportunities (not only for statisticians!) 37
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
6
8. ‘Data is [are] arguably the most important natural re-
source of this century. ... Big data is [are] big news
just about everywhere you go these days. Here in
Texas, everything is big, so we just call it data.’
Michael Dell, 2014
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
7
9. 1. Demystifying the ‘big data’ hype
• There is no question that ‘big data’ have hit the business, government and scientific
sectors.
Indeed, the term ‘big data’ has acquired the trappings of religion!
However, there are a lot of examples of companies that were into ‘big data’ before
they were called ‘big data’ — a term coined in 1998 by two researchers at SGI.
• But, what exactly are ‘big data’?
In short, the term ‘big data’ applies to information that can not be processed or
handled using traditional processes or tools.
Big data are an architecture and most related challenges are IT focused!
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
8
10. • The following characteristics — known as ‘the four Vs’ or ‘V4
’ — provide one
standard definition of big data:
– ‘Volume’ : ‘data at rest’, i.e. the amount of data ( ‘data explosion problem’);
– ‘Variety’ : ‘data in many forms’, i.e. different disparate types of data (e.g.
structured, semi-structured and unstructured, e.g. text, web or multimedia data
such as images, videos, audio), data sources (e.g. internal, external, public) and
data resolutions;
– ‘Velocity’ : ‘data in motion’, i.e. the speed by which data are generated and
need to be handled;
– ‘Veracity’ : ‘data in doubt’, i.e. the varying levels of noise and processing errors.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
9
11. Example. Possible challenges for life sciences’ data are ‘variety’ and ‘veracity’ (and
the related quality and correctness of the data).
Especially the combination of different data sources (e.g. combining omics data
generated by various high-throughput technologies) — resulting from ‘variety’ —
provides a lot of insights.
‘Volume’ and ‘velocity’ are often the least important issues for life sciences’ data.
• The above standard definition of big data is vulnerable to the criticism of sceptics
that these four Vs have always been there.
Nevertheless, the definition provides a clear and concise ‘business’ framework to
communicate about how to solve different data processing challenges.
But, what is new?
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
10
12. “Big Data’ ... is the simple yet seemingly revolutionary
belief that data are valuable. ... I believe that ‘big’
actually means important (think big deal). Scientists
have long known that data could create new knowledge
but now the rest of the world, including government
and management in particular, has realised that data
can create value.’
Source: interview with Sean Patrick Murphy, a senior scientist at Johns Hopkins University
Applied Physics Laboratory, in the Big Data Innovation Magazine, September 2013.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
11
13. ‘Big data and analytics based on it promise to change
virtually every industry and business function over the
next decade.’
Thomas H. Davenport and Jinho Kim, 2013
Source: Davenport, T. H. & Kim, J. (2013). Keeping Up with the Quants: Your Guide to
Understanding and Using Analytics. Boston, MA: Harvard Business Review Press.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
12
14. ‘The term ‘big data’ is going to disappear in the next
two years, to become just ‘data’ or ‘any data’. But, of
course, the analysis of data will continue.’
Donald Feinberg, 2014
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
13
15. ‘Data are not taken for museum purposes; they are
taken as a basis for doing something. ... The ultimate
purpose of taking data is to provide a basis for action
or a recommendation for action.’
W. Edwards Deming
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
14
16. 2. Demystifying the ‘data science’ hype
• The demand for ‘data scientists’ — the ‘magicians of the big data era’ — is
unprecedented in sectors where value, competitiveness and efficiency are driven by
data.
• The Data Science Association defined in October 2013 the terms ‘data science’ and
‘data scientist’ within their Data Science Code of Professional Conduct as follows (see
www.datascienceassn.org/code-of-conduct.html):
– Data science is the scientific study of the creation, validation
and transformation of data to create meaning.
– A data scientist is a professional who uses scientific methods
to liberate and create meaning from raw data.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
15
17. • ‘Data-Driven Decision making’ (DDD) refers to the practice of basing decisions
on data, rather than purely on intuition:
Source: Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
16
18. • Data science has been dubbed by the Harvard Business Review (Thomas H. Dav-
enport and D. J. Patil, October 2012) as
‘the sexiest job in the 21st century ’
and by the New York Times (April 11, 2013) as a
‘ hot new field [that] promises to revolutionise industries
from business to government, health care to academia’.
But, is data science really new and ‘sexy’?
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
17
19. • The term ‘data science’ was originally coined in 1998 by the statistician Chien-Fu
Jeff Wu when he gave his inaugural lecture at the University of Michigan.
Wu argued that statisticians should be renamed data scientists since they spent
most of their time manipulating and experimenting with data.
• In 2001, the statistician William S. Cleveland introduced the notion of ‘data science’
as an independent discipline.
Cleveland extended the field of statistics to incorporate ‘advances in computing
with data’ in his article ‘Data science: an action plan for expanding the technical
areas of the field of statistics’ (International Statistical Review, 69, 21–26).
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
18
20. • Although the term ‘data scientist’ may be relatively new, this profession has existed
for a long time!
For example, Johannes Kepler published his first two laws of planetary motion,
which describe the motion of planets around the sun, in 1609.
Kepler found them by analysing the astronomical observations of Tycho Brahe.
Kepler was clearly a data scientist!
Or, for example, Napoleon Bonaparte (‘Napoleon I’) used mathematical models to
help make decisions on battlefields.
These models were developed by mathematicians — Napoleon’s own data scien-
tists!
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
19
21. Another (famous) example of that same time period is the following map (‘carte
figurative’) drawn by the French engineer Charles Joseph Minard in 1861 to show the
tremendous losses of Napoleon’s army during his Russian campaign in 1812-1813,
where more than 97% of the soldiers died.
Sources: Van der Lans, R. (2013). The data scientist at work. BeyeNETWORK,
October 24, 2013 (www.b-eye-network.com/view/17102).
Tufte, E. R. (2001). The Visual Display of Quantitative Information
(2nd edition). Cheshire, CT: Graphics Press.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
20
22. Minard was clearly a data scientist!
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
21
23. ‘Data science in a way is much older than Kepler — I
sometimes say that data science is the ‘second oldest’
occupation.’
Gregory Piatetsky-Shapiro, November 26, 2013
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
22
24. ‘I keep saying the sexy job in the next ten years will be
statisticians.’
Hal Varian, 2009
Source: interview with Google’s chief economist in the McKinsey Quarterly, January 2009.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
23
25. ‘And with ongoing advances in high-performance com-
puting and the explosion of data, ... I would venture
to say that statistician could be the sexy job of the
century.’
James (Jim) Goodnight, 2010
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
24
26. ‘I think he [Hal Varian] is behind — using statistics
has been the sexy job of the last 30 years. It has just
taken awhile for organisations to catch on.’
James (Jim) Goodnight, 2011
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
25
27. • Looking at all the ‘crazy’ hype in the media around the terms ‘big data’ and ‘data
science’, it seems that ‘data scientist’ is just a ‘sexed up’ term for ‘statistician’.
It looks like statisticians just needed a good marketing campaign!
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
26
28. But, what is statistics ?
– Statistics can be defined as the science of ‘learning from data’ (or of making
sense out of data).
It includes everything from planning for the collection of data and subsequent
data management to end-of-the-line activities such as drawing conclusions of
numerical facts called data and presentation of results.
– Statistics is concerned with the study of uncertainty and with the study of deci-
sion making in the face of uncertainty.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
27
29. • However, data science is not just a rebranding of statistics , large-scale statistics
or statistical science!
• Data science is rather a rebranding of ‘data mining’!
‘The terms ‘data science’ and ‘data mining’ often are
used interchangeably, and the former has taken a life
of its own as various individuals and organisations try
to capitalise on the current hype surrounding it.’
Foster Provost and Tom Fawcett, 2013
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
28
30. ‘Data science is a train that is going 99% faster than
the rails its on can support. It could derail, go off a
cliff, and become nothing but a failed fad.’
Mark Biernbaum, 2013
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
29
31. ‘It is already time to kill the ‘data scientist’ title. ...
The data scientist term has come to mean almost any-
thing.’
Thomas H. Davenport, 2014
Source: Thomas H. Davenport’s article ‘It is already time to kill the ‘data scientist’ title’
in the Wall Street Journal, April 30, 2014.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
30
32. But, what is data mining?
Data mining is the non-trivial process of identifying valid, novel, poten-
tially useful, and ultimately understandable patterns or structures or models
or trends or relationships in data to enable data-driven decision making.
‘Non-trivial’: it is not a straightforward computation of predefined quantities like
computing the average value of a set of numbers.
‘Valid’: the patterns hold in general, i.e. being valid on new data in the face of
uncertainty.
‘Novel’: the patterns were not known beforehand.
‘Potentially useful’: lead to some benefit to the user.
‘Understandable’: the patterns are interpretable and comprehensible — if not
immediately then after some postprocessing.
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33. The data science (or data mining) Venn diagram
Source: Drew Conway, September 2010 (drewconway.com/zia/2013/3/26/the-data-science-venn-diagram).
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34. ‘Statistics has been the most successful information
science. Those who ignore statistics are condemned
to re-invent it.’
Brad Efron, 1997
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35. 3. What distinguishes data science from statistics?
• Statistics traditionally is concerned with analysing primary (e.g. experimental) data
that have been collected to explain and check the validity of specific existing ideas
(hypotheses).
Primary data analysis or top-down (explanatory and confirmatory) analysis.
‘Idea (hypothesis) evaluation or testing’ .
• Data science (or data mining), on the other hand, typically is concerned with
analysing secondary (e.g. observational) data that have been collected for other
reasons. The usage of these data is to create new ideas (hypotheses).
Secondary data analysis or bottom-up (exploratory and predictive) analysis.
‘Idea (hypothesis) generation’ .
Knowledge discovery.
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36. • The two approaches of ‘learning from data’ are complementary and should proceed
side by side — in order to enable proper data-driven decision making.
Example. When historical data are available the idea to be generated from a bottom-
up analysis (e.g. using a mixture of so-called ‘ensemble techniques’) could be
‘which are the most important (from a predictive point of view) factors
(among a ‘large’ list of candidate factors) that impact a given output?’.
Mixed with subject-matter knowledge this idea could result in a list of a ‘small’
number of factors (i.e. ‘the critical ones’).
The confirmatory tools of top-down analysis (statistical ‘Design Of Experiments’,
DOE, in most of the cases) could then be used to confirm and evaluate this idea.
By doing this, new data will be collected (about ‘all’ factors) and a bottom-up
analysis could be applied again — letting the data suggest new ideas to test.
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37. ‘Neither exploratory nor confirmatory is sufficient alone.
To try to replace either by the other is madness. We
need them both.’
John W. Tukey, 1980
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38. 4. Outlook, challenges and opportunities (not only for statisticians!)
• Data should be regarded as key strategic assets!
• Extracting useful knowledge from data to solve ‘business’ problems must be treated
systematically by following a process with reasonably well-defined stages.
Like statistics, data science (or data mining) is not only modelling and prediction,
nor a product that can be bought, but a whole iterative problem solving cycle/process
that must be mastered through team effort.
Phases of the reference model of the methodology called CRISP-DM (‘CRoss
Industry Standard Process for Data Mining’; see www.statoo.com/CRISP-DM.pdf):
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40. ‘If I had only one hour to save the world, I would spend
fifty-five minutes defining the problem, and only five
minutes finding the solution.’
Albert Einstein
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41. • One might rightly be sceptical about big data because the idea is still fuzzy and
there is an awful lot of marketing hype around.
However, big data are here to stay and will, over time, impact every single ‘busi-
ness’!
• There is convincing ‘evidence’ that data-driven decision making and big data tech-
nologies will substantially improve ‘business’ performance.
• Statistical rigour is necessary to justify the inferential leap from data to knowledge.
• Lack of expertise in statistics can lead (and has already led) to fundamental errors
(e.g. using omics data)!
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42. ‘To have any hope of extracting anything useful from
big data, ... , effective inferential skills are vital. That
is, at the heart of extracting value from big data lies
statistics.’
David J. Hand, 2014
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43. ‘Statisticians have spent the past 200 years figuring
out what traps lie in wait when we try to understand
the world through data. The data are bigger, faster
and cheaper these days — but we must not pretend
that the traps have all been made safe. They have not.’
Tim Harford, 2014
Source: Tim Harford’s article ‘Big data: are we making a big mistake?’ in the
Financial Times Magazine, March 28, 2014.
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44. ‘There are a lot of small data problems that occur in
big data. They do not disappear because you have got
lots of the stuff. They get worse.’
David Spiegelhalter, 2014
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45. • The challenges from a statistical perspective include
– the need to think really hard about a problem and to understand the underlying
mechanism that drive the processes that generate the data ( ask the ‘right’
question(s)!);
– the provenance of the data, e.g. the quality of the data, the characteristics of the
sample ( ‘sampling bias’, i.e. is the sample representative to the population it
was designed for?);
– the ethics of using (big) data, particularly in relation to personal data, i.e. ethical
issues related to privacy ( ‘information rules’ need to be defined), confiden-
tiality (of shared private information), transparency (e.g. of data uses and data
users) and identity (i.e. data should not compromise identity);
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46. – the visualisation of the data;
– the validity of generalisation, and the replicability and reproducibility of findings;
– the balance of humans and computers.
‘Data and algorithms alone will not fulfil the promises
of big data. Instead, it is creative humans who need
to think very hard about a problem and the underlying
mechanisms that drive those processes.’
David Park, 2014
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47. ‘The numbers have no way of speaking for themselves.
We speak for them. We imbue them with meaning. ...
Data-driven predictions can succeed — and they can
fail. It is when we deny our role in the process that
the odds of failure rise. Before we demand more of
our data, we need to demand more of ourselves.’
Nate Silver, 2012
Source: Silver, N. (2012). The Signal and The Noise: Why Most Predictions Fail but Some Do Not.
New York, NY: The Penguin Press.
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48. ‘As big data [data science] and statistics engage with
one another, it is critical to remember that the two
fields are united by one common goal: to draw reliable
conclusions from available data.’
Kaiser Fung, 2013
Source: Fung, K. (2013). The pending marriage of big data and statistics. Significance, 10(4), 22–25.
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49. ‘Most of my life I went to parties and heard a little
groan when people heard what I did. Now they are all
excited to meet me.’
Robert Tibshirani, 2012
Source: interview with Robert Tibshirani, a statistics professor at Stanford University,
in the New York Times on January 26, 2012.
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50. Have you been Statooed?
Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Morgenstrasse 129
3018 Berne
Switzerland
email kuonen@statoo.com
@DiegoKuonen
web www.statoo.info
/Statoo.Consulting
51. Copyright c 2001–2014 by Statoo Consulting, Switzerland. All rights reserved.
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