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Doubt and Verify: Data Science Power Tools

Authors:
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Doubt$Everything:$Enter$Evidence@Based$Reasoning$
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Data$Science("C(%&$")/&".&=,-(2-=6+7&'()&%+2>(-?#+7&.",&'==6/-(@&)'%'A-(%+(7-B+&'('6/7-7&%"&
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Do$Teachers$and$Professionals$Have$to$Lie?$
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Epilogue$
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... That is, you discover what the models and methods are designed to discover. One must be objective in data science across the entire workflow -data selection, preparation, modelling, analysis, and interpretation; hence, a data scientist must always Doubt and Verify (Brodie, 2015b). ...
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Data Science, a new discovery paradigm, is potentially one of the most significant advances of the early 21 st century. Originating in scientific discovery, it is being applied to every human endeavor for which there is adequate data. While remarkable successes have been achieved, even greater claims have been made. Benefits, challenge, and risks abound. The science underlying data science has yet to emerge. Maturity is more than a decade away. This claim is based firstly on observing the centuries-long developments of its predecessor paradigms-empirical, theoretical, and Jim Gray's Fourth Paradigm of Scientific Discovery (Hey, Tansley & Tolle, 2009) (aka eScience, data-intensive, computational, procedural); and secondly on my studies of over 150 data science use cases, several data science-based startups, and, on my scientific advisory role for Insight 1 , a
... The scientific 6 2 About the same time as Tukey used "data science" in reference to statistics, Peter Naur in Sweden used the term (interchangeably with "datalogy") to refer to computer science (Sveinsdottir & Frøkjaer, 1988 This anchoring of the modern understanding of data science more in business than in academia is the main reason for many of the references in this work pointing to blog posts and newspaper articles discussion, once in a leading role, had difficulty to keep up with the dynamics of 2010-2015 and followed with some delay (Provost & Fawcett, 2013;Stadelmann et al., 2013). It is currently accelerating again (see Brodie (2015b) and his chapters later in this book). ...
Chapter
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What is data science? Attempts to define it can be made in one (prolonged) sentence, while it may take a whole book to demonstrate the meaning of this definition. This book introduces data science in an applied setting, by first giving a coherent overview of the background in Part I, and then presenting the nuts and bolts of the discipline by means of diverse use cases in Part II; finally, specific and insightful lessons learned are distilled in Part III. This chapter introduces the book and provides an answer to the following questions: What is data science? Where does it come from? What are its connections to big data and other mega trends? We claim that multidisciplinary roots and a focus on creating value lead to a discipline in the making that is inherently an interdisciplinary, applied science.
... That is, you discover what the models and methods are designed to discover. One must be objective in data science across the entire workflow -data selection, preparation, modelling, analysis, and interpretation; hence, a data scientist must always Doubt and Verify (Brodie, 2015b). ...
Chapter
Full-text available
Data science, a new discovery paradigm, is potentially one of the most significant advances of the early twenty-first century. Originating in scientific discovery, it is being applied to every human endeavor for which there is adequate data. While remarkable successes have been achieved, even greater claims have been made. Benefits, challenge, and risks abound. The science underlying data science has yet to emerge. Maturity is more than a decade away. This claim is based firstly on observing the centuries-long developments of its predecessor paradigms—empirical, theoretical, and Jim Gray’s Fourth Paradigm of Scientific Discovery (Hey et al., The fourth paradigm: data-intensive scientific discovery Edited by Microsoft Research, 2009) (aka eScience, data-intensive, computational, procedural)—and secondly on my studies of over 150 data science use cases, several data science-based startups, and, on my scientific advisory role for Insight (https://www.insight-centre.org/), a Data Science Research Institute (DSRI) that requires that I understand the opportunities, state of the art, and research challenges for the emerging discipline of data science. This chapter addresses essential questions for a DSRI: What is data science? What is world-class data science research? A companion chapter (Brodie, On Developing Data Science, in Braschler et al. (Eds.), Applied data science – Lessons learned for the data-driven business, Springer 2019) addresses the development of data science applications and of the data science discipline itself.
... Beide Konzepte in ihrer heute populären Form entstanden dabei aus den Bedürfnissen der Wirtschaft heraus [Patil, 2011]. Eine wissenschaftliche Auseinandersetzung folgte etwas verzögert und nimmt aktuell an Fahrt auf [Brodie, 2015a]. Von Beginn an wurden sie jedoch begleitet von enormer medialer Beachtung bis hin zum Hype [z.B. ...
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Data Scientists sind gefragt: Laut Mc Kinsey Global Institute wird es in den nächsten Jahren allein in den USA einen Nachfrageüberschuss an 190.000 Data Scientists geben. Dieser sehr starke Nachfragetrend zeigt sich auch in Europa und im Speziellen in der Schweiz. Doch was verbirgt sich hinter einem Data Scientist und wie kann man sich zum Data Scientist ausbilden lassen? In diesem Kapitel definieren wir die Begriffe Data Science und das zugehörige Berufsbild des Data Scientists. Danach analysieren wir drei typische Use Cases und zeigen auf, wie Data Science zur praktischen Anwendung kommt. Im letzten Teil des Kapitels berichten wir über unsere Erfahrungen aus dem schweizweit ersten Diploma of Advanced Studies (DAS) in Data Science, das an der ZHAW im Herbst 2014 erstmals gestartet ist.
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What is a data scientist? How can you become one? How can you form a team of data scientists that fits your organization? In this chapter, we trace the skillset of a successful data scientist and define the necessary competencies. We give a disambiguation to other historically or contemporary definitions of the term and show how a career as a data scientist might get started. Finally, we will answer the third question, that is, how to build analytics teams within a data-driven organization.
Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies
Bringing evidence-­-based education to CS
M. Guzdial. 2015. Bringing evidence-­-based education to CS. Commun. ACM 58, 6 (May 2015). DOI:http://dx.doi.org/10.1145/2783419.2754947