Imagine stepping back in a time machine to, say, 2012, and falling into a conversation with someone who tells you that self-driven cars will soon become a reality. Back then, most of us would quietly assume that our interlocutor had perhaps watched the movie Minority Report a little too often. Fast-forward again to the present, however, and that conclusion looks decidedly outdated. The fact is that in 2023, automobile colossus Hyundai Motors, in collaboration with world leader in driverless tech Motional, is expected to unveil Level 4 “autonomous vehicles” across a raft of major American cities.
As the MSN report citing this development noted, “There’s a team of data scientists behind this innovation that has worked with other teams to make this a reality. And a clear example of how data science is a stepping-stone for the future.”
The article goes on to refer to a recent U.S. Bureau of Labor Statistics report forecasting the creation of 11.5 million well-paid jobs in data science by 2026.
The need for Big Data experts such as data scientists is growing quickly as technological innovations such as Artificial Intelligence, robotics, blockchain and the Internet of Things advance onwards at an accelerating pace.
With this trend in mind, let’s explore what data science is, what qualifications are needed to practice it, and how it’s changing the way decisions are made at the highest of levels.
Just what is data science?
Data scientists are tasked with capturing, exploring, analyzing and interpreting the stupendous volumes of data generated by modern technologies. With methods and tools acquired during their advanced training, they discern previously undetected patterns in these oceans of gigabytes. From these, they extract meaningful information to enable decision-makers to formulate optimal courses of action. As a field of study and practice, data science also uses sophisticated machine learning algorithms to construct predictive models that would otherwise elude the capabilities of human cognition.
The data under study is derived from a multitude of sources and comes to the data scientist in a wide range of different formats. With advanced knowledge of machine learning, mathematical modeling, statistical methods, computer programming languages and database management, data scientists are called upon to capture raw structured and unstructured data and store it securely (“data warehousing”) but also to perform skilled operations upon it to make it intelligible to human minds. These include data cleansing (a form of “sorting the wheat from the chaff”, separating the valuable data from the “noise”) and data processing – “mining” it out of vast mountains of gigabytes, classifying and clustering it, modeling realistic future trajectories, and putting it in a form that key decision-makers can use to guide their judgments.
For those who are drawn to this emerging, high-demand profession, it may come as a pleasant surprise to find that although an advanced degree in the subject is a key gateway, studying for it won’t necessarily involve giving up one’s existing career and traveling to and from a university campus for a full-time program.
Thanks to the availability of an online master’s degree in data science provided by, for example, world-class centers of academic excellence such as California’s Worcester Polytechnic Institute (WPI), which is widely recognized for its influential faculty and accomplished alumni, a far more flexible option is available.
This particular degree, which delivers all the coveted knowledge fields required for a data scientist in depth, can be studied 100% online around existing job obligations from the comfort of one’s own home. As an online program, it allows students who are parents to still be around for their kids’ school sports days and tuck them into bed at night. Candidates lacking prior experience in math or programming will find the course offers built-in bridge courses to fill any gaps in knowledge. There are no application fees and no commuting expenses to and from campus.
How Data Science is changing decision-making at the highest level
Entrepreneurs have always sought to quantify information as accurately as possible in order to bring clarity out of jumble. The era of digital data on the scale of terabytes has made that infinitely more complicated. Here are some of the ways in which data scientists “step into the breach”, so to speak, and render data mountains intelligible for actionable insights.
Enabling better decisions by managers: Data scientists often find themselves called upon to help advise staff in an organization on how to optimize their abilities in analytics. This typically entails communicating and demonstrating the value of the establishment’s data for decision-making processes across the entire organization. In doing so, data scientists will often need to simplify complex concepts drawn from their analyses of the data. This may involve the use of visual aids (such as histograms, pie charts and cluster diagrams) and “translating” the patterns into narratives – a form of story-telling – to bring the secrets within the data to life vividly. This makes the data more widely intelligible to non-specialist colleagues.
Helping define goals by identifying trends: Data scientists frequently identify and construct model extrapolations of trends that imply recommending specific options for action to decision-makers. The aim is to enhance an organization’s performance, improve customer engagement and/or boost profitability.
Helping colleagues adopt best practices and identify the most salient issues: By continually foregrounding the value of an organization’s analytics product and demonstrating its practical usefulness, data scientists assist colleagues in understanding the product’s capabilities more fully. In turn, this helps them to extract insights they would otherwise not have gleaned in order to deal with business challenges more successfully.
Discerning innovations and new opportunities: By bringing their expert knowledge to bear on an institution’s existing analytics system, data scientists often find themselves in a somewhat “Socratic” role; Socrates famously kept asking questions to challenge presuppositions and expose blind spots. They can question existing processes as few others can, enabling them to continually improve the value extracted from an institution’s data.
Corrective feedback following decisions: Although data scientists are tasked with coming up with recommendations for decision options based on their analytics, their work doesn’t stop there. They keep an analytic eye on how those decisions are panning out once implemented, measuring key metrics and outcomes so that corrective measures can be taken if required.
These and many other data-rooted functions are becoming essential, not secondary, concerns for modern businesses. The era of the data scientist has arrived.