Examples of using Data scientists in English and their translations into Hebrew
{-}
-
Colloquial
-
Ecclesiastic
-
Computer
-
Programming
Data scientists are generally excited;
Historically, it's been difficult to find data scientists.
Data scientists are required in every industry.
For example, there will be more jobs for data scientists.
From data scientists, I have seen two common misunderstandings.
But, companies large and small need more than data scientists;
Many data scientists see a cool new machine learning problem.
The skill-sets and competencies that data scientists employ vary widely.
In contrast,Splunk provides a machine learning toolkit that requires data scientists.
Data scientists are going to be among the most demanded specialists in the hi-tech market.
This second group resists an easy name, but I will call them data scientists.
What happens when data scientists crunch through three centuries of Robinson Crusoe?
Working with technology teams, management and/or data scientists to set goals.
Good data scientists are able to apply their skills to achieve a broad spectrum of end results.
It first appeared in 1991 andhas become extremely popular among data scientists.
Work with IT teams, management and/ or data scientists to determine organizational goals.
Data scientists might call these characteristics“features” and social scientists would call them“variables.”.
We need purpose-built machine learning that can be used by security and operations professionals,not data scientists.
Data scientists might call these characteristics“features” and social scientists would call them“variables.”.
Clay Sciences provides a solution to aproblem its founders frequently encountered while working as data scientists.
Data scientists determine which variables, or attributes, should be analyzed and which model should be used to build forecasts.
He asserts that a scientist is“a new breed”,and that“a shortage of data scientists is becoming a serious constraint in some sectors”.
On the other hand, data scientists are quick to point out the benefits of repurposeddata while ignoring its weaknesses.
Xaxis combines proprietary technology, unique data assets andexclusive media relationships with the brightest team of audience analysts, data scientists and software engineers.
In Actiview, a group of Data Scientists assist organizations to analyze the statistics and to improve the business operations based on data analysis.
Finding the right algorithm is partly just trial and error- even highly experienced data scientists can't tell whether an algorithm will work without trying it out.
Data Scientists tend to focus on the translation of Big Data into Business Intelligence, while Data Engineers focus much more on building the Data Architecture and infrastructure for data generation.