March 22, 2017, 12:39 a.m.
The data science world is split into two parts: the (i)Python and the R community. Both groups offer a plethora of tools and libraries enriching our work-life as a data scientist.
Interestingly, many of the offerings are complementary, such that professional data scientists should know both environments to pick the right tool for the job. In many cases, it even makes sense to use Python and R together in the same project.
Sadly, today these two worlds don’t integrate very well, so we need to switch back and forth between different tools and environments.
March 19, 2017, 2:38 a.m.
On R-Blogger there was an interesting article that compares debugging support for R in RStudio and Eclipse StatET . I liked that article very much, but it misses the new RIDE environment, which I am going to add to the comparison in this article.
March 9, 2017, 11:22 p.m.
Models in Finance
Dual Time Dynamic (DtD) model presented by Breeden (2007) decomposes vintage level data to months-on-books (maturation), calendar date (exogenous) and vintage (quality). In this blog, I demonstrate how we can use APC library to apply DtD models in credit risk management and stress testing.