RSS Featured Blog Posts
  • 3 Reasons to Learn the Expected Value Framework for Data Analysis
    One of the most difficult and most critical parts of implementing data science in business is quantifying the return-on-investment or ROI.  In this article, we highlight three reasons you need to learn the Expected Value Framework, a framework that connects the machine learning classification model to ROI.  …
    Matt Dancho
  • Remotely Send R and Python Execution to SQL Server from Jupyter Notebooks
    Introduction Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, […]
    Kyle Weller
  • Weekly Digest, July 16
    Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. Featured Resources and Technical Contributions Best Machine Learning Tools:…
    Vincent Granville
  • Critically Reading Scientific Papers
    Critically reading scientific papers is critical for Data Scientists working some areas - especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers: Theory: Is the theory sound? Are there theoretical issues in the design that cause…
    Howard Friedman
  • The art of data science...
    In 2018, Fast Company declared ‘Data Scientist’ as the best job in America for the third…
    Ziyad Nazem

We noticed your search to hire data scientists, and no doubt you realize there is more demand for this expertise than the available pool of talent. In addition, while the practice of data science is not new, the professionals calling themselves data scientists come from diverse backgrounds, differing levels of education and experience. Some will argue that econometrics is the best background for a data scientist, while others view statistics with computer science skills, or teach yourself programming it’s the creative mindset applied to business and knowledge of mathematical applications that are the most brilliant. Suffice it to say, data science has multiple approaches to solve a problem, and combined training in the discipline of econometrics, mathematics and statistics with computer science chops and field experience is going to provide a candidate with the best approach to solving your business problems over and over again. So, let’s agree it’s a strategic hire for your organization.

The Data Science Beagle has teamed up with our friends at Divergence Academy, and offers several programs available to assist you in your search, either fee based, contingent or sponsorship.

 

 

 

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