Broad strokes

With the technological advances of recent years, especially for the reason that turn of the millennium, data science has grow to be a discipline in its own right, separate from computer science and more closely aligned to statistics. It has carved out a distinct segment for itself where data scientists apply themselves to solving business problems that depend on the access, processing and ultimately the interpretation of knowledge.

This demands a specific skillset resembling a very good understanding of programming languages, for instance Python and R, to assist simplify analytics workflows required to access large disparate data sets. The information scientist’s skillset combined with that of the economist delivers a winning formula for those looking to differentiate themselves from the herd and master modern economics.

Facts & figures

Findings above are supported by the proven fact that the celebrated London School of Economics [1] has prolonged its curriculum lately to incorporate an undergraduate degree titled the BSc Data Science and Business Analytics, with the strapline promising learners that they might “learn to analyse data to deal with real world-problems”, real-world problems that are naturally based in economic and business relations.

One other positive indicator is that the previous Chief Economist of the World Bank [2] and the joint-winner of the 2018 Nobel Memorial Prize in Economic Sciences, Paul Romer, is a proponent of Jupyter Notebook, an open source web application which allows users to create and share documents including live code, equations and visualisations to support interactive computing across multiple programming languages. This final remark is on the core of Jupyter, the name Jupyter being an acronym meaning Julia, Python and R, all three being programming languages.

For a large in Economics to be a vocal advocate of a knowledge science tool speaks volumes — no pun intended — and it clearly indicates the direction of travel. As Romer noted on a blogpost back in 2018: “Jupyter rewards transparency; Mathematica rationalizes secrecy. Jupyter encourages individual integrity; Mathematica lets individuals hide behind corporate evasion”. [3] Here he’s comparing Jupyter with a competing platform, Mathematica, nonetheless if…