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Domain expert data science. The importance of domain knowledge a healthcare data science perspective november 10 2017 3 comments in data science data science news gerneral insights main category projectmanagement use case use cases by thomas blanchard. Focusing on a single domain not only increases your intellectual ability but also gives you a rich reach in the wider field of data science. Equipped with these tools engineers can partner with or serve as data scientists to. A data scientist who has domain expertise sufficient to understanding and acting successfully on a given business objective and who can talk the domain expert s language stands a much greater chance of developing a tool which will work properly and be more readily adopted within the organization.
I ve realized during my masters that it is impossible to be a domain expert in. Others myself included argue that the engagement of the domain expert while extensive will need to be more iterative increasing in scope as domain experts learn to think more like data scientists. The term domain knowledge has been in play even before data science became popular. So if data scientists succeed in providing an advanced data enabled decision machine to these business experts when they need it and where they need it then the data scientists have proved their worth.
Become a data science expert. Know your domain train constantly show your skills. In this article i argue for these two points and provide a best practise basis for modern data science projects. In software engineering it means the knowledge about the environment in which the target i e.
Some argue that the role of the domain expert needs to be extensive in all stages of the design and implementation process. Plan build a strategy prioritize. This must be provided by a domain expert making data science projects a team effort. The case for more domain experts in data science p by angela guess kalev leetaru recently wrote in forbes as i ve come to work with an ever widening swath of the data sciences and big data communities i have been struck by how narrowly focused much of its practitioner base is on statistics and computational expertise as opposed to.
These type of roles focus on leveraging the tools and data provided by the other members of the data science.
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