Data Science: The Modern-day Pillar of Economics | by Petru van der Walt Félix | Nov, 2023

Broad strokes

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

This demands a particular skillset such as a good understanding of programming languages, for example Python and R, to help simplify analytics workflows required to access large disparate data sets. The data scientist’s skillset combined with that of the economist delivers a winning formula for those looking to distinguish themselves from the herd and master modern economics.

Facts & figures

Findings above are supported by the fact that the prestigious London School of Economics [1] has extended its curriculum in recent years to include an undergraduate degree titled the BSc Data Science and Business Analytics, with the strapline promising learners that they would “learn to analyse data to address real world-problems”, real-world problems which are naturally based in economic and business relations.

Another positive indicator is that the former 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 at the core of Jupyter, the name Jupyter being an acronym meaning Julia, Python and R, all three being programming languages.

For a giant in Economics to be a vocal advocate of a data 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 is comparing Jupyter with a competing platform, Mathematica, however if…