Examples of using Data scientists in English and their translations into Bengali
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Colloquial
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Ecclesiastic
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Computer
Data scientists.
The new world is Data Scientists.
Data scientists are generally excited;
I have two more messages, one for the data scientists out there.
Data scientists: we should not be the arbiters of truth.
This second group resists an easy name, but I will call them 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.
Data scientists use it to gather and analyze data. .
Thus, social researchwill be shaped by both social scientists and data scientists.
Many data scientists see a cool new machine learning problem.
Thus, social researchwill be shaped by both social scientists and data scientists.
From data scientists, I have seen two common misunderstandings.
In my experience, social scientists and data scientists approach to this repurposing very differently.
Data Scientists heavily benefit from a broad subject matter expertise area.
On the other hand, ML engineers are mainlytasked with creating tools that are used by data scientists.
Data scientists, however, have less training and experience studying social behavior.
Christopher Olah's blog isone of the most sought-after reading materials for data scientists worldwide.
The second main solution is to do what data scientists call user-attribute inference and what social scientists call imputation.
Optimistic: The two communities that this book engages- social scientists and data scientists- have very different styles.
Good data scientists will not just address business problems; they will pick the right problems that have the most value to the organization.
Algorithmic confounding is relatively unknown to social scientists, but it is a major concern among careful data scientists.
Data scientists, on the other hand, have little systematic experience with research ethics because it is not commonly discussed in computer science and engineering.
Optimistic: The two communities that this book engages- social scientists and data scientists- have very different styles.
The second misunderstanding that I have seen from data scientists is thinking that social science is just a bunch of fancy-talk wrapped around common sense.
It is for social scientists that want to do more data science,and it is for data scientists that want to do more social science.
This contrast also captures a difference between data scientists, who tend to work with Readymades, and social scientists, who tend to work with Custommades.
Optimistic: The two communities that this book engages- social scientists and data scientists- have very different backgrounds and interests.
And I will add that mostcompanies don't have embarrassing lawsuits, but the data scientists in those companies are told to follow the data, to focus on accuracy.