Data scientist: the sexiest job of the 21st century.

By T. Davenport, D. J. P. Artwork, Tamar Cohen, Andrew J Buboltz. Harvard Business Review, 2012

783 citations recorded by the source corpus.

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Citations in the corpus

Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management (2013, background): “For instance, the October 2012 issue of Harvard Business Review contained three articles that are relevant to this editorial: “Big Data: The Management Revolution” (McAfee and Brynjolfsson 2012), “Data Scientist: The Sexiest Job of the 21st Century” (Davenport and Patil 2012), and “Making Advanced A”
Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies (2018, background): “While much discussion has focused around the role of the data scientist in contemporary organizations (Davenport and Patil 2012), recent literature also expanded on the role of managers in the age of big data (Rans-botham et al. 2015).”
Social Injustice in Surveillance Capitalism (2017, background): “Although computer coding and data analytics have always been rarefied domains, the rapid rise of big data along with the development of new tools and algorithms to handle massive unstructured datasets has shrunk it drastically (see Davenport and Patil 2012).”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, unknown): “In a highly influential article, Davenport and Patil (2012) address the important role that the emerging job of the data scientist will have in the context of big data.”
The production of prediction: What does machine learning want? (2015, background): “…1997), the abundant software libraries, the machine learning and data mining competitions (Dahl, 2013) and the many university curricula and online training courses focused on data science and the training of data scientists, ‘the sexiest career of the 21st century’ (Davenport and Patil, 2012).”
'Big data', Hadoop and cloud computing in genomics (2013, methodology): “J Biomed Inform (2013), http://dx.d 10.1016/j.jbi.2013.07.001 Aisling O’Driscoll a, Jurate Daugelaite b, Roy D. Sleator b,⇑ a Department of Computing, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland b Department of Biological Sciences, Cork Institute of Technology, Rossa Avenu”
Design of the 2015 ChaLearn AutoML challenge (2015, background): “In apparent contradiction with this perceived simplicity of the problem, we read in the press that there is presently a shortage of data scientists to analyze big data [1].”
How to improve firm performance using big data analytics capability and business strategy alignment (2016, background): “For example, data scientists at Yahoo developed Apache Hadoop and at Facebook created the Hive language for Apache Hadoop projects—the path has been followed by other data-driven companies, such as Google, Amazon, Walmart, eBay, LinkedIn and Twitter, to transform their big data analytics capability ”
Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “The shift towards schema-less data storage and management coupled with organizations’ increasing desire to leverage big data as a source of competitive advantage has brought about the rise of data scientists (Davenport & Patil, 2012; McAffe & Brynjolfsson, 2012).”
Management challenges in creating value from business analytics (2017, background): “They cite Barton and Court (2012), Chen et al. (2012), Davenport and Patil (2012) and McAfee and Brynjolfsson (2012) as recent evidence of that importance and note Waller and Fawcett discuss the importance of DPB to their particular functional community of logistics and supply chain management…”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “In a highly influential article, Davenport and Patil [38] address the important role that the emerging job of the data scientist will have in the context of big data.”