Multilevel Modelling of U.S. Home Loan Data
The housing market has undergone quite a change in the past decade, with more stringent lending criteria for housing having been enforced. A key objective of financial in...
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Exploratory Data Analysis with Python
In addition to being the sexiest job of the twenty-first century, Data Science is new electricity as quoted by Andrew Ng. A lot of professionals from various discipline...
Writing/Reading Large R dataframes/data.tables — Addendum.
After posting my most recent blog using census data to illustrate handling “large” dataframes in R exploiting fst and feather file formats, I realized I c...
Simple Trick to Normalize Correlations, R-squared, and so on
Many statistics, such as correlations or R-squared, depend on the sample size, making it difficult to compare values computed on two data sets of different sizes. Here, w...
Build vs. Buy: The Conundrum Facing the Commercial Insurance Industry as It Embraces an AI-Based Future
Artificial intelligence (AI) seemingly has been discussed everywhere over the last few years, and now it’s made its way into the commercial insurance industry. Organiza...
Making New Drugs with a Dose of Artificial Intelligence
Originally posted by John Bowden. There is a meeting of the world’s most renowned scientists once in two years. These meetings are held to tackle biological puzzles t...
Economic Value of Learning and Why Google Open Sourced TensorFlow
Blog key points: Google open-sourced TensorFlow to gain tens of thousands of more users across hundreds (thousands) of new use cases to improve the predictive effectivene...
Using a Schema to Construct a Metrics Regime
A schema is a conceptual framework. It can function as a lens through which to study data. When I was conducting research on workplace stress to do my graduate degree...
10 Myths About Data Scientists
In the nascent field of Data Science, myths are abound. Here’s my top 10, scoured from the internet (where better than to find a myth or two?). Myth #1: It’s ...
#DevOps for Machine Learning (#ML / #AI)
Until very recently, most organizations have seen two distinct, non-overlapping work streams when building an AI enabled application: a development path and a data scienc...