Collection, manipulation and interpretation of analyzed Information (data) has been a crucial part of many economic and social processes the world over. This has been the major reason why every successful corporate (and economy) has continuously sought to make use of every relevant, available piece of information to make decisions that aim to improve profitability and competitive advantage among other things.
It was through this need for harnessing the power of information for entities’ own use that data science; an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from a particular set of data was born. In Zimbabwe, the name is relatively unknown and the underlying principles behind the science even more hidden.
Illustrative examples of data science in action include aspects such as digital advertising where while online, e.g. on Facebook one can get targeted advertising (or friend requests) based on their previous web activity such as visited sites. Recommendations can also be given for example on what content to watch on YouTube based on videos one would have watched before.
Data science has wide application areas including finance, human resources and marketing. Companies can determine which of their products are best for upselling and the ones best for cross-selling based on data science analytics. Another example is where asset management companies can use data science to predict the likelihood of a security’s price going up or down. I could spend the whole day talking about what data science can do and the huge impact it has already made on making companies more effective and efficient in various aspects.
Now that we have uncovered the myth behind the practice, (or have we?), it brings us to the next question: what are the required skills for this magnificent modern day technological practice?
A typical data scientist is kind of a hybrid between a statistician and a computer programmer or an acceptable range of similar combinations. The individual ideally has a combination of predictive modelling and machine learning skills that provide him with the capacity to conduct analysis beyond your ordinary statistician or any other quantitative analyst. You could be forgiven for not having an idea of what machine learning is because you are in Zimbabwe, but because Techzim already wrote about it, not so much…
Going back to our initial postulation of a virtual drought of the knowledge and practice of data science in Zimbabwe (save for some of us Google fanatics), this is a grim reality which urgently needs a level-headed get-together by the academia ( so far no university in the country to my knowledge offers a program which satisfactorily equips students with the requisite skills), professional bodies ( imagine most actuarial functions in the country are still being performed using Microsoft Excel) and the corporate world so as to set the agenda for developing a promotional program as well as setting milestones for engraving data science practice into different economic sectors.
Regardless of the fact that Zimbabwe is still coming up technologically and the economy is determined not so much by free market forces as by government regulation and political pronouncements (sic), there is need for the various economic and social sectors of the economy to tap into the potential data science functions that promote informed decision-making at management level leading to efficiency and effectiveness as well as greater profitability.
University students studying statistics, mathematics, actuarial science, computer science or any of their combinations need not be taught only to work out equations, statistical distributions and theorems, but also need applied knowledge on C++, R, Python programming, database management systems, VBA and et cetera, which excites them and makes them much more useful in their professional practice.
Sadly this mutual understanding by various stakeholders in the country on the importance and need to promote data science will take forever, but a gleam of hope for enthusiasts, for now, is offered by free online free learning platforms such as FutureLearn, edX and Coursera among others.
About Author: Nhamo Dapi is an actuarial analyst, researcher and writer with interests in I.T based software solutions, he can be contacted at firstname.lastname@example.org.