In the middle of deploying the model, team of data scientists realize
that the predictions are "somewhat-off". Troubleshooting on the horizon
and what to do. Session will guide you through most common mistakes data
scientists and statisticians are making when preparing and engineering
the data using T-SQL or any other database system.
Further more, we will
explore common statistical and data science mistakes when modeling
data, extracting know-how from the data, finding the hidden patterns and
running different test against the structural models using mainly R,
Python, or Spark. What not-to-do will be replaced with what to-do
explanations using sample datasets and sample codes.
Some statistical
knowledge or background is a plus!