RSS Featured Blog Posts
  • Free Book: Introduction to Statistics
    Online Statistics Education: A Multimedia Course of Study.  Project Leader: David M. Lane, Rice University. Content: Introduction Graphing Distributions Summarizing Distributions Describing Bivariate Data Probability Research Design Normal Distributions Advanced Graphs Sampling…
    Capri Granville
  • Introduction to Deep Learning
    Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks, etc.).…
    Vincent Granville
  • The Fourth Way to Practice Data Science – Purpose Built Analytic Modules
    Summary:  Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies.   It appears that data science has…
    William Vorhies
  • Weekly Digest, September 17
    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. Announcement Enterprise AI: Take the Plunge. Data science and advanced analytics front-runner Dataiku announces the release of…
    Vincent Granville
  • Landing a 150k USD #datascientist job: Seven things you need to know
      On Linkedin, many Data Science enthusiasts who aspire to be Data Scientists follow me.   One person asked the question:   What do I need to know to get a $150K job as a Data Scientist?   It’s a good…
    ajit jaokar

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