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Continuous data can answer questions like: what is the staff turnover at a factory?
The trouble with the question 'Which hypothesis is correct given the totality of the data?' is of course the one that Hume saw: if the domain is unbounded, no finite amount of data can answer the question.
An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment.
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By analyzing that data, we can answer questions like "where do people like to go out on summer week days in Berlin?", "what are the most popular weekend past-times in NYC?", "who else takes photos of geometric shapes and nature?" Our philosophy has always been to attract users as organically as possible.
The SOSD does not contain data that can answer why this might be.
Data scientists need to be carefully equipped with proper tools and access to company-wide data so they can answer ad hoc questions, perform exploratory data mining, support BI teams, and help with data productization.
With more data, sales associates can answer customer questions, upsell and cross-sell more effectively, and truly delight customers.
After decades of what at first amounted to little more than guesswork, scientists are collecting data they think can answer those questions.
We should point out that it is well-known that one can pre-process a tree in linear time and space to produce a data structure that can answer any LCA query on that tree in constant time [ 37- 39].
Technical data can never answer purely conceptual questions.
Furthermore, 14 years of data can successfully answer the two main questions of our study.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com