Data science needs a place on the STEM curriculum Credit: Image credit: Alex via Flickr
Training and Development

Data science needs a place on the STEM curriculum

This is a contributed piece from Mark Palmer, SVP and General Manager of Analytics, at TIBCO Software

The Harvard Business Review once called data science the ‘sexiest job in the 21st century’. With the current generation of children growing up in a world with data science all around them – Netflix telling them which movies to watch, online retailers suggesting the perfect shoes to complement an outfit  - it is hardly surprising that it’s an appealing career option for today’s tech-savvy youngsters. Why then, are we not routinely teaching it?

Careers in data science are comparatively well paid, considered desirable and rapidly growing in number, so now really is the time to encourage making data science a core component of a Science, Technology, Engineering and Mathematics (STEM) curriculum.

As an academic field, data science crosses multiple disciplines, requiring modules of statistics, analytics, computer science and maths. This multi-disciplinary nature, combined with the relative infancy of data science, has left universities scratching their heads as they attempt to define it and develop curriculums.

So let us start by defining data science. One definition is: ‘the field of extracting knowledge and insights from data in various forms’. This is unlikely to ignite the collective imaginations of teenagers’ across the world, so perhaps a more exciting visual explanation might work, showing exactly what data science can do.

Yet, the code below appears far from compelling or awe-inspiring.

 

following-code

However, when this very same piece of code is transformed into one of the most iconic graphical representations of all time - known as ‘the map that made a nation cry’ - its power to excite brings alive the data science promise.

like-this

 

Joseph Minard’s 1869 cartographic depiction of numerical data uses numbers to tell the devastating tale of Napoleon’s failed attack on Russia between 1812 and 1813. It shows (from left to right in brown) 422,000 French soldiers entering Russia in 1812. The line narrows, representing the huge loss of life during the campaign, with only 100,000 men remaining as the French arrive in Moscow.

Study the graphic further and you see that the black represents their retreat, and gets thinner as it moves west. Below the path of retreat, the graph adds greater levels of information, detailing time and temperature. This provides the harrowing insight that when the French left Moscow, the temperature was 0, then dipped as low as -30 degrees, a scenario the French were not prepared for. A year later, when the French army exited Russia, there remains only a thin line, representing 10,000 men as 412,000 men had perished in battle or due to the extreme weather.

This cartographic depiction shows the power of data science. The graphic shows six dimensions of data in one stunning image: the number of troops, their direction of travel, their location, the temperature, the time, and situation relative to specific dates. It brings the code to life.

Minard’s work was certainly ground-breaking back in the nineteenth century, but where are we now? Today, between one and two trillion statistical graphics like this are created every year, thanks to the power of computing and volumes of data. Statistical graphics are powerfully applied to almost every element of our lives and the world around us. They can influence all sorts of change in the world, as they are used to predict crop yields, help discover new drugs and everything in between.

In terms of our working lives, data science undoubtedly impacts every profession, from entry-level office workers to CEOs. Surely the need for data workers will go through the roof as businesses now have new forms of data available to them at a scale that is historically unprecedented, given that over the past couple of decades, everything has become connected—the internet, mobile phones, embedded sensors and so on. Every device generates data and all that data needs to be analysed. That’s a lot of data, with more on the way, and lots of data scientists are needed to make use of it all.

Data science will continue to become even more critical as technology advances. It is set to be pivotal in all businesses in the future, yet in the meantime, there looks to be a massive shortage of data science experts in the making. A respected career that is well paid, high profile and fun; data science deserves a place on every STEM education curriculum.

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