I’m a new graduate from MEF University in the department of economics. I am currently working in EY. My working fields are credit risk and market risk. I’m interested in derivative products and I’m responsible for derivative valuations in EY. My journey of data science has started with Ozgur Hoca. After I met him, I’ve become a data enthusiast and taken many IE courses.
Digiscape is one of 8 Future Science Platforms in CSIRO focussed on delivering new analytics in the digital age to better inform agricultural systems in the face of uncertainty. The Uncertainty Toolbox is one of 15 projects within Digiscape trying to make a difference to the way models are interpreted, reported and communicated in practice for decision-making. Uncertainty is front and centre of every modelling problem but it is sometimes difficult to quantify and challenging to communicate. The Sensitivity Analysis workflow focuses on developing a general framework for sensitivity analysis to inform the modeller about key parameters of interest and refine the model so it can be used in a robust way to make predictions and forecasts with uncertainties. They outline the design steps for constructing workflow using the latest object oriented systems available in R and give a demonstration of the tool using Shiny.
In his book, Extending R, John Chambers writes: One of the attractions of R has always been the ability to compute an interesting result quickly. R developers have taken the challenge and have integrated R with some really good stuff while providing easy access that conforms to natural R workflows. Rcpp and Shiny, for example, are both spectacularly successful projects in which R developers expanded the reach of R by connecting to external resources.
A 2017 report by StackOverflow showed that the use of R is greatest and growing fastest in academia. Commercial industries like tech, media, and finance, however, show the smallest usage and lowest adoption rates of the language. Yet learnings regarding the use of R and data science in academia and commercial settings complement each other. He discussed how the cutting-edge R skills used in academia can improve commercial product development. He also identified the knowledge gaps he had moving into commercial business.