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What is hardcore data science – in practice?

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For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.


A Domain-Independent Production-Rule System for Consultation Programs. William van MeIle, Aug 1979 card 1 of 1

Classics (Collection 2)

William van Melte Heuristic Programming Project Department of Computer Sc;ence Stanford University Stanford, California 94305 Abstract EMYCIN is a programming system for writing knowledge-based consultation programs with a production-rule representation of knowledge. Several major components of the system, Including an explanation program and knowledge acquisition routines, are described. EMYCIN has been used to build consultation systems in several areas of medicine, as well as an engineering domain. These experiences lead to some general conclusions regarding the potential applicability of EMYCIN to new domains. Keywords: knowledge-based systems, production rules, knowledge representation, automated consultant. This work was supported in part by the NSF grant Advanced Research Projects Agency, contract and the 1 Introduction The focus of much current work In artificial intelligence is the development of computer programs that aid scientists with complex reascning tasks.