Examples of using Data scientists in English and their translations into Urdu
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Data scientists.
Employ resources with expert domain knowledge rather than“data scientists”.
From data scientists, I have seen two common misunderstandings.
This second group resists an easy name, but I will call them data scientists.
Many data scientists see a cool new machine learning problem.
People also translate
Thus, social research will be shaped by both social scientists and data scientists.
Data scientists are generally excited; they tend to see the glass as half full.
Thus, social research will be shaped by both social scientists and data scientists.
Data scientists solve complex data problems through employing deep expertise in some scientific discipline.
In my experience, social scientists and data scientists approach to this repurposing very differently.
Algorithmic confounding is relatively unknown to social scientists, but it is a major concern among careful data scientists.
The second main solution is to do what data scientists call user-attribute inference and what social scientists call imputation.
Spotfire, in my opinion is a tool best aimed at those users who are true Data Analysts orusing the new buzzord“Data Scientists“.
On the other hand, data scientists are quick to point out the benefits of repurposeddata while ignoring its weaknesses.
Teams are setup and architects busily start white-boarding, data scientists are recruited, fresh and eager straight from University.
Algorithmic confounding is relatively unknown to social scientists, but it is a major concern among careful data scientists.
It has been my experience that many social scientists and data scientists view these ethical issues as a swamp to be avoided.
On the other hand, data scientists are typically quick to point out the benefits of repurposed data while ignoring its weaknesses.
One barrier to creating theseshared standards is that social scientists and data scientists tend to have different approaches to research ethics.
Data scientists, on the other hand, have little systematic experience with research ethics because it is not commonly discussed in computer science and engineering.
This contrast also captures a difference between data scientists, who tend to work with Readymades, and social scientists, who tend to work with Custommades.
The company announced in April that it is expanding its Irish operation and seeking 175 new hires,looking particularly for software engineers, data scientists and blockchain specialists.
Professors andstudents may rely on the pro bono support from our big data scientists to develop researches and customized business cases for their stakeholders' needs.
But, as I will show in detail in Chapter 6(Ethics) this approach seriously limited in ways that arenot widely appreciated by both social scientists and data scientists.
R's a great choice for basic data analysis and visualization work andis widely used by Data Scientists- the other competing data science language is Python.
It is generally expected that data scientists are able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects are not required.
As that happens, I expect that the rules-based approach of social scientists andthe ad-hoc approach of data scientists will evolve toward something like the principles-based approached described in Chapter 6.
Data scientists apply their analytical training to information stored in data structures through expressions and algorithms crafted in programming languages heavily used in the profession, like R, Python and Java.
As that happens, I expect that the rules-based approach of social scientists andthe ad hoc approach of data scientists will evolve toward something like the principles-based approached described in chapter 6.
Of three kinds of incompleteness, the problem of incomplete data to operationalize theoretical constructs is the hardest to solve, and in my experience,it is often accidentally overlooked by data scientists.