Data Science

Data Science











tificial Intelligence (AI) which includes Machine learning (ML) and





Data Science

Data science has gained popularity for Artificial Intelligence (AI) which includes Machine Learning (ML) and Deep Learning which can be can be supervised, semi-supervised or unsupervised. Key for businesses are regression and multivariant regression (projecting numbers, profiling, predicting and/or recommending).

“CFOs have been asking more and more about artificial intelligence (AI), machine learning, robotics and advanced analytics, says Alex Bant, Chief of Research, Finance, Gartner. 2021 is the year to pivot from discussions about the future to making real investments, seeing short-term wins and cost off-set, and having a clear plan for the future.”  Gartner - Top Priorities for Finance Leaders in 2021

High-dimensional Statistics

Modern Data Science makes possible newer and valuable capabilities like categorization or recommendation engines. We see these engines in play when shopping online with similar product recommendations or ads being displayed. These engines are capable of understanding and associating people to products or people to people because of high-dimensional statistics. This is the big win! It is difficult for people to see and comprehend more than two dimensions (it is too complex for the human mind). We typically use a two-dimensional (2D) space or plane or X,Y plot to correlate or associate variables. Moving beyond two can get difficult to understand. Many dimensions, dates and numerics exist in Enterprise and Big Data environments today for this type of analysis. Data Science can assist in the database with computations across many records, dimensions, dates and numerics.

We assist with increasing enterprise use with Descriptive and Inferential Statistics on Enterprise Data Warehouses (EDWs) and supporting Data Lakes.