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Definition:<\/strong><\/p>\n A data scientist<\/strong> is a professional who uses statistics, programming and contextual knowledge to formulate questions, analyse data, develop and evaluate models and communicate results.<\/p>\n Their work may use structured, semi-structured or unstructured data. The role does not necessarily require large data volumes or machine learning in every project: responsibilities depend on the problem and the organisation.<\/strong><\/p>\n\n The process begins by turning a general requirement into a question that can be investigated using data. Before selecting a technique, the expected outcome, analysed population, constraints and evaluation criteria need to be defined.<\/p>\n The work may include:<\/p>\n The United States Bureau of Labor Statistics includes identifying useful data, analysing it, creating and validating models and presenting findings among these professionals’ duties. Its occupational description of data scientists<\/a> illustrates the breadth of the role without establishing one mandatory workflow.<\/p>\n The boundaries between job titles are not universal.<\/strong> Two organisations may use the same title for different responsibilities or distribute one project across several roles.<\/p>\n A data analyst commonly focuses on queries, reports, visualisations and descriptive or diagnostic analysis. A data scientist may also perform these tasks but typically incorporates experimentation, inference or predictive modelling when the problem requires it.<\/p>\n A data engineer designs and maintains systems for ingestion, transformation, storage and availability. A data scientist consumes these structures and may prepare specific datasets but does not necessarily replace engineering work.<\/p>\n A machine learning engineer commonly focuses on turning models into reliable, scalable and observable software components. A data scientist may create prototypes and evaluate models, while operating them in production also requires engineering, security and operational work.<\/p>\n Business intelligence<\/a> organises data, indicators and reports to support monitoring and decisions. It may share tools and sources with data science but is not equivalent to every statistical analysis or predictive model.<\/p>\n Statistics supports sample design, relationship estimation, uncertainty quantification and result evaluation. Programming supports querying, transforming and analysing information through languages and environments such as SQL, Python or R.<\/p>\n Data mining<\/a> provides methods for discovering patterns in information. Machine learning<\/a> supports systems that estimate outcomes or classify cases from data. Both disciplines may form part of the work but do not define the role by themselves.<\/p>\n Domain knowledge helps interpret variables, detect incorrect assumptions and assess whether a relationship is meaningful. Communication allows the result to be explained to people who did not participate in the analysis and prevents an estimate from being presented as a certainty.<\/p>\n The particular tools depend on the sources, volume, frequency, technical environment and intended use. A project also needs documentation, version control, transformation traceability and the ability to reproduce results.<\/p>\n A model needs to be evaluated with data and metrics appropriate to its purpose. A strong technical score does not automatically demonstrate usefulness, profitability or impact. Simpler alternatives and the procedure used before the model should also be compared.<\/p>\n Prediction and causation are different objectives.<\/strong> A model may anticipate an outcome without demonstrating what causes it. Causal decisions require additional designs, assumptions and evidence.<\/p>\n Data may contain errors, omissions and imbalances or represent only part of the population. These conditions may affect groups, periods and situations not observed during development in different ways.<\/p>\n When an analysis uses personal or confidential data, its purpose, access, retention, security and deletion options need to be defined. Reusing information for a different objective is not justified merely because it is technically possible.<\/p>\n Deployed models require monitoring because data, processes and behaviour may change. Automation does not remove the responsibility to review errors, document decisions and control the system’s consequences.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" Definition: A data scientist is a professional who uses statistics, programming and contextual knowledge to formulate questions, analyse data, develop and evaluate models and communicate results. Their work may use structured, semi-structured or unstructured data. The role does not necessarily require large data volumes or machine learning in every project: responsibilities depend on the problem […]<\/p>\n","protected":false},"author":6,"featured_media":0,"template":"","encyclopedia-tag":[1302],"class_list":["post-20211","encyclopedia","type-encyclopedia","status-publish","hentry","encyclopedia-tag-data-science"],"_links":{"self":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia\/20211","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia"}],"about":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/types\/encyclopedia"}],"author":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/users\/6"}],"wp:attachment":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media?parent=20211"}],"wp:term":[{"taxonomy":"encyclopedia-tag","embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia-tag?post=20211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}How a data scientist works<\/h2>\n
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Differences from other data roles<\/h2>\n
Data scientist competencies<\/h2>\n
Data work governance<\/h2>\n