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Data Engineer vs Data Scientist

DATA DATA Vs ENGINEER SCIENTIST Int. *** Analyzing the existing data architecture and collecting requirements for the new one Understand business problems and research industries; Cooperating with backend engineers to build a high-quality data architecture Frame business problems into mathematical problems; Determining what data is necessary to solve a particular business problem Testing and maintaining the infrastructure to make sure that it consistently delivers high-quality data Continually dealing with large Control the architecture's volumes of data and performance in regards to business metrics - engineers measure how architecture implementation helped achieve specific goals organizing them properly Setting up long-term strategies for data management that can be used by a team for years Identify opportunities for data collection and finding Managing machine learning and statistical tools for precise analysis, sometimes even writing own frameworks cost-efficient and ethical ways to leverage them Identify requirements for all data processing - production, mining, modeling Set up predictive analytics and find ways to leverage the existing data to forecast future changes; Choosing tools for all data-related processes Employ metrics that check the accuracy of these Introducing innovation and automation to the Data scientist vs machine infrastructure learning engineer have different responsibilities, which means, data scientists can, but don't have to write ML frameworks Create long-term data quality requirements and determine actions that help achieve it Cooperate with data engineers providing them with insights for infrastructure improvement Constantly improve the infrastructure to get higher-quality data Being up to data with the latest changes in the infrastructure and data Define metrics for identifying data-quality, as the complexity of the management standards in the organization Jelvix

Data Engineer vs Data Scientist

shared by jelvix on Nov 23
Data engineers and scientists should cooperate, improve tools, infrastructure, and grow skillsets.


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