RSS Featured Blog Posts
  • Poker, Probability, Monte Carlo, and R
    My daughter just started a business analytics Master's program. For the probability sequence of the core statistics course, one of her assignments is to calculate the probability of single …
    steve miller
  • What a CEO needs to know about Machine Learning algorithms
    During my first project in McKinsey in 2011, I served the CEO of a bank regarding his small business strategy. I wanted to run a linear regression on the bank's data but my boss told me: "Don't do it. They don't understand statistics". (We did not use Machine…
    Pedro URIA RECIO
  • Are You Ready To Become A Chief Data Scientist?
    You know who you are. A high-calibre machine learning magician, a well-versed wrangler of data... but you want a bit more from your role. That may be progression, more money or the chance to work on new, more exciting projects, but where do you go from here?   Many companies are looking to increase investment […]
    Matt Reaney
  • Artificial Intelligence (AI) in Retail Market to hit $8bn by 2024
    Artificial Intelligence (AI) in Retail Market size is set to exceed USD 8 billion by 2024; according to a new research report by Global Market Insights, Inc.  The AI in retail market is driven by the increasing investments in it across the globe. The growing investment in the technology is attributed to the wide applications […]
  • Summarize and explore the data using SmartEDA
    Created an R package for exploratory data analysis. Package name is SmartEDA now available on CRAN. This package includes multiple custom functions to perform initial exploratory analysis on any input data describing the structure and the relationships present in the data. The generated output can be obtained in both summary and graphical form. The graphical form […]

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