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 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/.
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/.
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/.
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 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/.
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 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 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/.
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/.
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/.
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 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/.
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 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 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/.
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' 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/.
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 (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.
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
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/.
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/.
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.
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/.
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' 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/.
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 (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.
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
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/.
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
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The Age of Data Driven Science and Engineering Persontyle
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Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
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Einstein published his ideas and became a pivotal element in shifting the way we think about physics - from the Newtonian model to the Quantum - in turn this changed the way we think about the world and allowed us to develop new ways of engaging with the world.
We are at a similar juncture. The development of computational technologies allows us to think about astronomical volumes of data and to make meaning of that data.
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Statistics: Visualizing Data
Introductory Essay from the Locks
The Reality Today
All of us now are being blasted by information design. It's being poured into our eyes
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a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a
clearing in the jungle. –David McCandless
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We’re just learning how to till this soil and make sense of the mountains of data constantly being
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Many people, when they think of data, think of tables filled with numbers. But this long-held notion is
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A Statistician's 'Big Tent' View on Big Data and Data Science (Version 5)
1. A Statistician's `Big Tent' View on
Big Data and Data Science
(Version 5 as of 18.11.2014)
Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Statistical Consulting + Data Analysis + Data Mining Services
Morgenstrasse 129, 3018 Berne, Switzerland
@DiegoKuonen + kuonen@statoo.com + www.statoo.info
`Research Seminar in Statistics', University of Geneva, Switzerland | November 21, 2014
2. Abstract
There is no question that big data have hit the business, gov-
ernment and scienti
3. c sectors. The demand for skills in data
science is unprecedented in sectors where value, competitiveness
and eciency 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.
4. About myself (about.me/DiegoKuonen)
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.
Senior Lecturer in Statistics, Geneva School of Economics and Management,
University of Geneva, Switzerland.
President of the Swiss Statistical Society.
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2
5. About Statoo Consulting (www.statoo.info)
Founded Statoo Consulting in 2001.
Statoo Consulting is a software-vendor independent Swiss consulting
6. rm spe-
cialised in statistical consulting and training, data analysis and data mining ser-
vices.
Statoo Consulting oers 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?
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3
7. 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;
Credit Lyonnais, United Kingdom;
CSL Behring;
F. Homann-La Roche;
GfK Telecontrol;
GlaxoSmithKline, United Kingdom;
H. Lundbeck, Denmark;
Lombard Odier Darier Hentsch;
Lonza;
McNeil, Sweden;
Merck Serono;
Nagra Kudelski Group;
Nestle;
Nestle Frisco Findus;
Nestle Research Center;
Novartis Pharma;
Novelis;
P
8. zer, United Kingdom;
Philip Morris International;
Pioneer Hi-Bred, Germany;
PostFinance;
Procter Gamble, Germany;
publisuisse;
Rodenstock, Germany;
Sano
9. -Aventis, France;
SAP France and SAP Ireland;
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;
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4
10. as well as Swiss and international government agencies, nonpro
11. t organisa-
tions, oces, 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 Oce 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 Oce for Migration;
Swiss Federal Research Stations Agroscope Changins{Wadenswil and Reckenholz{
Tanikon;
Swiss Federal Statistical Oce;
Swiss Institute of Bioinformatics;
Swiss State Secretariat for Economic Aairs (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.
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13. `Normality is a myth; there never was, and never will
be, a normal distribution.'
Robert C. Geary, 1947
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14. Contents
Contents 8
1. Demystifying the `big data' hype 10
2. Demystifying the `data science' hype 19
3. What distinguishes data science from statistics? 36
4. Conclusion and opportunities (not only for statisticians!) 39
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15. `Data is arguably the most important natural resource
of this century. ... Big data is big news just about ev-
erywhere you go these days. Here in Texas, everything
is big, so we just call it data.'
Michael Dell, 2014
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16. 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 1997 by two researchers at the NASA.
But, what exactly are `big data'?
In short, the term `big data' applies to an accumulation of data that can not be
processed or handled using traditional data management processes or tools.
Big data are a data management architecture and most related challenges are IT
focused!
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10
18. nition of big data:
{ `Volume' : `data at rest', i.e. the amount of data ( `data explosion problem'),
with respect to the number of observations ( `size' of the data), but also with
respect to the number of variables ( `dimensionality' of the data);
{ `Variety' : `data in many forms' or `mixed data', i.e. dierent types of data
(e.g. structured, semi-structured and unstructured, e.g. log
19. les, text, web or
multimedia data such as images, videos, audio), data sources (e.g. internal,
external, open, public) and data resolutions;
{ `Velocity' : `data in motion', i.e. the speed by which data are generated and
need to be handled (e.g. streaming data from machines, sensors and social data);
{ `Veracity' : `data in doubt', i.e. the varying levels of noise and processing errors.
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20. `While its size receives all the attention, the most di-
cult aspect of big data really involves its lack of struc-
ture.'
Thomas H. Davenport, 2014
Source: Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the
Opportunities. Boston, MA: Harvard Business School Press.
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22. nitely not a requirement to
have a minimum of a petabyte of data, say.
Bigger challenges are `variety' and `velocity', and possibly most important is `ve-
racity' and the related quality and correctness of the data.
Especially the combination of dierent data sources (such as combining company
data with social networking data and public data) | resulting from `variety' |
provides a lot of insights and this can also happen with `smaller' data sets.
The above standard de
23. nition of big data is vulnerable to the criticism of sceptics
that these four Vs have always been there.
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24. `The only thing really new about big data is the cool
new name.'
Daniel J. Solove, September 16, 2014
Nevertheless, the above definition of big data provides a clear and concise `business'
framework to communicate about how to solve different data processing challenges.
But, what is new?
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25. 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 former senior scientist at Johns Hopkins University
Applied Physics Laboratory, in the Big Data Innovation Magazine, September 2013.
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26. `Suddenly it makes economic sense to try to extract
value from all this data out there.'
Sean Owen, 2014
Source: interview with Sean Owen, Cloudera's director of data science, in Research Live, May 19, 2014.
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27. `The term `big data' is going to disappear in the next
two years, to become just `data' or `any data'. '
Donald Feinberg, 2014
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28. `Data are not taken for museum purposes; they are
taken as a basis for doing something. If nothing is to
be done with the data, then there is no use in collecting
any. The ultimate purpose of taking data is to pro-
vide a basis for action or a recommendation for action.'
W. Edwards Deming, 1942
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29. 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 eciency are driven by
data.
The Data Science Association de
30. ned 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 scienti
31. c methods
to liberate and create meaning from raw data.
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32. `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.
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34. 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
35. eld [that] promises to revolutionise industries
from business to government, health care to academia'.
But, is data science really new and `sexy'?
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36. 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
37. eld of statistics' (International Statistical Review, 69, 21{26).
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38. 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!
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39. `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
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40. `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.
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41. `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
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42. `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
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43. Looking at all the `crazy' hype 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!
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44. But, what is statistics ?
{ Statistics can be de
45. ned as the science of `learning from data' (or of making
sense out of data), and of measuring, controlling and communicating uncertainty.
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.
{ Uncertainty is measured in units of probability, which is the currency of statistics.
{ Statistics is concerned with the study of uncertainty and with the study of deci-
sion making in the face of uncertainty.
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46. 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
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47. `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.
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48. 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 prede
49. ned 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 bene
50. t to the user.
`Understandable': the patterns are interpretable and comprehensible | if not
immediately then after some postprocessing.
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51. 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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52. `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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53. 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 (and not `under control' of the investigator) to create new ideas (hypotheses).
Secondary data analysis or bottom-up (exploratory and predictive) analysis.
`Idea (hypothesis) generation' .
Knowledge discovery.
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54. 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 con
55. rmatory tools of top-down analysis (statistical `Design Of Experiments',
DOE, in most of the cases) could then be used to con
56. rm 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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57. `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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58. 4. Conclusion and opportunities (not only for statisticians!)
Data, and the capability to extract useful knowledge from data, should be regarded
as key strategic assets!
Decision making that was once based on hunches and intuition should be driven
by data and knowledge ( data-driven decision making).
Extracting useful knowledge from data to solve `business' problems must be treated
systematically by following a process with reasonably well-de
59. ned 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 interdisciplinary and transdisciplinary team effort.
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60. 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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61. `If I had only one hour to save the world, I would spend
66. nding the solution.'
Albert Einstein
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67. 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'!
The `age of big data' will (hopefully) be a golden era for statistics.
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.
That is, at the heart of extracting value from big data lies statistics.
Lack of expertise in statistics can lead (and has already led) to fundamental errors!
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68. `We are in the era of big data, and big data needs
statisticians to make sense of it.'
Eric Schmidt and Jonathan Rosenberg, 2014
Source: Eric Schmidt, Google's chairman and former CEO, and Jonathan Rosenberg, Google's
former senior vice president of product, in their 2014 book How Google Works
(New York, NY: Grand Central Publishing | HowGoogleWorks.net).
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69. `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
70. `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
71. 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 | including issues like
omissions, data linkage errors, measurement errors, censoring, missing observa-
tions, atypical observations, missing variables ( `omitted variable bias'), the
characteristics and heterogeneity of the sample | big data being `only' a sample
(at a particular time) of a population of interest ( `sampling/selection bias',
i.e. is the sample representative to the population it was designed for?);
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46
72. { the ethics of using and linking (big) data, particularly in relation to personal data,
i.e. ethical issues related to privacy ( `information rules' need to be de
73. ned),
confidentiality (of shared private information), transparency (e.g. of data uses
and data users) and identity (i.e. data should not compromise identity);
{ the visualisation of the data;
{ spurious (false) associations ( `coincidence' increases, i.e. it becomes more
likely, as sample size increases) versus valid causal relationships ( `con
75. cation of (and the controlling for) confounding factors;
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47
76. { multiple statistical hypothesis testing for explanation (not prediction!) with tens
of thousands or even millions of tests performed simultaneously , often with com-
plex dependencies between tests (e.g. spatial or temporal dependence), and the
development of further statistically valid methods to solve large-scale simultane-
ous hypothesis testing problems;
{ the dimensionality of the data ( `curse of dimensionality', i.e. data become
more `sparse' or spread out as the dimensionality increases), and the related usage
and development of statistically valid strategies for dimensionality reduction, e.g.
using `embedded' variable subset selection methods like `ensemble techniques';
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48
78. tting'), and the replicability and
reproducibility of findings;
{ the nature of uncertainty (both random and systematic);
{ the balance of humans and computers.
`Data and algorithms alone will not ful
79. l 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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49
80. `We give numbers their voice, draw inferences from
them, and define their meaning through our interpreta-
tions. Hidden biases in both the collection and analysis
stages present considerable risks, and are as important
to the big data equation as the numbers themselves.'
Kate Crawford, 2013
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50
81. `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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51
82. `As you plan your big data analytics journey, make sure
you know your business goals, understand the data life-
cycle and have a plan for delivering insights to every-
one who needs them. Taking a holistic approach to big
data analytics will be an important step in addressing
technical challenges and reaching your business goals.'
John Whittaker, 2014
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52
83. `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, January 26, 2012.
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53
84. 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
85. Copyright
c 2001{2014 by Statoo Consulting, Switzerland. All rights reserved.
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