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

The Data Science Interview – Classroom Assessment

  1. Send your data science candidate to a full day assessment workshop. They will be given a complex problem to solve that assesses their capabilities across disciplines. $1750 per interview
  2. Based on our classroom observations we will provide a rating of Data Science Leader, Data Science Expert, Data Engineer, Data Analyst or Not Recommend.
  3. Ratings
    • Data Science Leader – technically skilled, creative problem solver, effective communicator, applies the appropriate data science models to solve various business problems and personality to work well with team and management
    • Data Science Expert – technically skilled, creative problem solver, applies the appropriate models to solve various business problems
    • Data Engineer – technically skilled, works well with data integration aspects within the assessment, requires more cross disciplined experience to fill a data scientist role
    • Data Analyst – technically adept, creative with data visualization, effective communicator at translating data outcomes, lacks computer science skills, requires skill development and cross discipline experience to fill a data scientist role