Examples of using Data scientists in English and their translations into Hindi
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Computer
They call themselves data scientists.
Are data scientists learning on the job?
MT: What are the challenges for hiring analysts and data scientists?
That's why citizen data scientists play such a critical role.
Data scientists are going to be among the most demanded specialists in the hi-tech market.
Certainly this was not something I could do manually,or even with the help from the team of data scientists I managed.
Citizen data scientists are not intended to replace data scientists. .
It has been my experience that many social scientists and data scientists view these ethical issues as a swamp to be avoided.
Data scientists analyzed 10,222 words to discover the"happiest" word in the English language.
The degrees invites you to enter the revolutionaryarea of big data where there is an acute shortage of data scientists.
In fact, 43 percent of data scientists are using R to solve statistical problems.
These professionals may vary in their skills and are likely toconsist of software developers, computer scientists and data scientists.
Our developers and data scientists create custom tools to help us see usage patterns.
According to a research report published by an international organization,the average annual salary of data scientists globally in the year 2015 was $130,000.
Today's students are tomorrow's data scientists, and“the only way to learn data science is BY DOING data science.”.
With the right software solution in place,everyday business users can become citizen data scientists, working smarter and empowered by data. .
Most Data Scientists have a strong background in mathematics or other domains of science and have a distinct possibility of PHD.
Many analytics providers these days are trying new ways of presenting the flood of complex data to marketers andbusiness managers who are not data scientists.
Data scientists have to be in a position to code prototype quick solutions, along with integrate with complex data systems.
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.
Data scientists, on the other hand, have little systematic experience with research ethics because it is not commonly discussed in computer science and engineering.
Neither of these approaches-the rules-based approach of social scientists or the ad-hoc approach of data scientists- is well suited for social research in the digital age.
In 2014, the increase in supply of data scientists and the introduction of new data mining tools will help bridge the productivity gap.
Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes.
On the other hand, data scientists are typically quick to point out the benefits of repurposed data while ignoring its weaknesses.
Data Scientists and Data Analysts are different in the sense that the Data Scientist starts by asking the right questions, the Data Analyst starts with Data Mining.
By some estimates, data scientists spend around 80% of their time on repetitive and tedious tasks that can be fully or partially automated.
All good Data Scientists differ in their skills and chosen technologies, but one thing they all share is a deep understanding of statistics.
That is, data scientists at Facebook knew of the empirical and theoretical research about transitivity and then baked it into how Facebook works.
The program intends to build Data Scientists whose solid technical background is complemented by a multidisciplinary preparation on various fields in which big data emerge.