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Articles related to "scientist"


How Data Science will Impact Future of Businesses?

  • Data science needs to find different ways to build machine learning models to convince business users and to make users easier to trust.
  • Depending on the requirements, the data scientist can work closely with the software developers to help everyone on the team reach the goal rather than needing any specific skill set.
  • Automated machine learning tools aim at eliminating elements of algorithm selection, iterative modeling, hyperparate tuning, model evaluation, and even data preparation in order to speed up the overall process and some of the complex aspects for the setup.
  • With a growing list of visible pre-trained models, firms are able to use it for sentiment analysis, image classification and text without the need for large label datasets and data science resources that are needed to train a complex model.

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How Data Science will Impact Future of Businesses?

  • Data science needs to find different ways to build machine learning models to convince business users and to make users easier to trust.
  • Depending on the requirements, the data scientist can work closely with the software developers to help everyone on the team reach the goal rather than needing any specific skill set.
  • Automated machine learning tools aim at eliminating elements of algorithm selection, iterative modeling, hyperparate tuning, model evaluation, and even data preparation in order to speed up the overall process and some of the complex aspects for the setup.
  • With a growing list of visible pre-trained models, firms are able to use it for sentiment analysis, image classification and text without the need for large label datasets and data science resources that are needed to train a complex model.

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How Much Data Engineering Does A Data Scientist Need To Know?

  • Companies using these platforms will often encourage their Data Science team to use their cloud infrastructure to develop their models and even deploy them into production using CI/CD pipelines.
  • Even though I am not a big fan of ready-made Data Science solutions, I must mention that cloud infrastructures now have rather advanced Machine Learning Platforms.
  • For Data Scientists, Spark (or PySpark for Python users) can be used to apply machine learning models on very large volumes of data (including streaming data!) while Data Engineers will use it to build reliable data pipelines.
  • While ETL jobs are NOT their responsibility, Data Scientists can make use of Airflow’s functionalities to automate their data processing for any Machine Learning model.
  • While Data Scientists should keep an eye on the evolution of Data Engineering & some of the tools involved, they will rarely need advanced knowledge in most of the mentioned software or systems.

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How Data Science will Impact Future of Businesses?

  • Data science needs to find different ways to build machine learning models to convince business users and to make users easier to trust.
  • Depending on the requirements, the data scientist can work closely with the software developers to help everyone on the team reach the goal rather than needing any specific skill set.
  • Automated machine learning tools aim at eliminating elements of algorithm selection, iterative modeling, hyperparate tuning, model evaluation, and even data preparation in order to speed up the overall process and some of the complex aspects for the setup.
  • With a growing list of visible pre-trained models, firms are able to use it for sentiment analysis, image classification and text without the need for large label datasets and data science resources that are needed to train a complex model.

save | comments | report | share on


How Much Data Engineering Does A Data Scientist Need To Know?

  • Companies using these platforms will often encourage their Data Science team to use their cloud infrastructure to develop their models and even deploy them into production using CI/CD pipelines.
  • Even though I am not a big fan of ready-made Data Science solutions, I must mention that cloud infrastructures now have rather advanced Machine Learning Platforms.
  • For Data Scientists, Spark (or PySpark for Python users) can be used to apply machine learning models on very large volumes of data (including streaming data!) while Data Engineers will use it to build reliable data pipelines.
  • While ETL jobs are NOT their responsibility, Data Scientists can make use of Airflow’s functionalities to automate their data processing for any Machine Learning model.
  • While Data Scientists should keep an eye on the evolution of Data Engineering & some of the tools involved, they will rarely need advanced knowledge in most of the mentioned software or systems.

save | comments | report | share on


How Much Data Engineering Does A Data Scientist Need To Know?

  • Companies using these platforms will often encourage their Data Science team to use their cloud infrastructure to develop their models and even deploy them into production using CI/CD pipelines.
  • Even though I am not a big fan of ready-made Data Science solutions, I must mention that cloud infrastructures now have rather advanced Machine Learning Platforms.
  • For Data Scientists, Spark (or PySpark for Python users) can be used to apply machine learning models on very large volumes of data (including streaming data!) while Data Engineers will use it to build reliable data pipelines.
  • While ETL jobs are NOT their responsibility, Data Scientists can make use of Airflow’s functionalities to automate their data processing for any Machine Learning model.
  • While Data Scientists should keep an eye on the evolution of Data Engineering & some of the tools involved, they will rarely need advanced knowledge in most of the mentioned software or systems.

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Ecological grief’ grips scientists witnessing Great Barrier Reef’s decline

  • Barnes decided to interview scientists and others working on the reef to investigate their response to this climate-change-driven catastrophe.
  • Recognizing how ecosystem decline and climate-related events can affect mental health is important, says Neville Ellis, a social scientist at the University of Western Australia in Perth.
  • He and Ashlee Cunsolo, who studies environmental change and health at Memorial University of Newfoundland in St John’s, Canada, wrote a commentary in Nature Climate Change1 last year that introduced the idea of ecological grief as an emotional side effect of environmental degradation.
  • Ellis notes that research such as Barnes’s highlights the emotional vulnerability of scientists who work at the forefront of an ecological crisis.
  • More people will be exposed to ecological loss as climate change intensifies, and researchers need a better understanding of how scientists and the public can maintain their well-being in the face of these challenges, says Ellis.

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Donald Trump's 'locked and loaded' threat doesn't, uh, mean what you think it means?

  • See, what the President meant by saying "locked and loaded" actually had nothing at all to do with a threat of military strike!
  • The sheer gall of Short to claim that "locked and loaded" has nothing to do with a military threat is only possible in an administration and with a President who has deeply denigrated the idea of facts and truth.
  • Let's say Russian President Vladimir Putin was quoted saying that Russia was "locked and loaded" to respond if the US continued to push the idea that they sought to meddle in the 2016 election.
  • (They did -- obviously.) Would you assume that by "locked and loaded" what Putin was doing was using a "broad term" that didn't connote the possibility of a military strike?
  • We all knew the second Trump tweeted the term "locked and loaded" what he meant.

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