Our world today is driven by data to such an extent that scientists estimate that the total global volume of the stuff will reach a staggering 180 zettabytes (that’s 180 trillion gigabytes) by 2025. And that leads to one certain consequence: demand for domain experts capable of interpreting and converting that galaxy of data into actionable insights is set to soar.
In 2022, for example, a report from the US Bureau of Labor Statistics found that demand for specialists in data science will have grown by 27.9% by 2026. As Business Insider notes, due to this extraordinarily rapid growth, “companies are facing a talent shortage of experienced data scientists due to evolving technology and inflated salaries”.
In the same article, lead data scientist Libby Kinsey, who heads data science at the UK tech firm Ocado, is quoted as saying: “It’s a new discipline, it’s been changing quite a lot. So it’s just quite hard to find the right people with the right skills.”
For those considering a change of career, data science looks firmly on course to become an exceptionally productive (and increasingly lucratively rewarded) professional discipline to move into. In the US, for example, a qualified data analyst can expect to command an average salary of $88,550, while that figure rises to $122,840 per year for a data scientist.
Such a career leap might sound like a fanciful idea. How, after all, can people in full-time employment who may also have family commitments gain the advanced qualifications necessary for such a skillful profession? However, there is a real, practical option – and that’s to embark on an online degree.
An example is the online Masters in Data Science from California’s Worcester Polytechnic Institute (WPI), a world-class center of academic excellence recognized for its influential faculty and accomplished alumni. The flexible delivery of this advanced degree makes it possible for adults with work and family obligations to fit studying for their new profession around their existing commitments. This coveted qualification can be earned 100% online, and offers built-in bridge courses for applicants without a relevant undergraduate degree.
Let’s take a deeper dive into the top-tier skills in data science that are so sought after.
The most sought-after skills that make candidates for data scientist roles stand out
Data scientists go beyond the routines of merely storing and managing banks of data. They’re equipped to analyze them and glean insights from them – insights that yield information intelligible to human minds, but that also drive business value in commercial settings.
The core skills that data scientists bring to the table include deep familiarity with a toolkit of scientific methodologies, statistical methods and data analytics. These comprise the foundational knowledge they draw upon when confronted with vast repositories of structured and unstructured data to find otherwise elusive jewels of insight buried inside the colossal mounds of gigabytes before them. Many data scientists possess, as a result of their training, competencies in mathematics, statistics, data mining, algorithms and advanced analytics. More recently, they have added competencies in machine learning (ML) – and, to some extent more recently, artificial intelligence (AI) – to their formidable skills repertoire.
Now may be a good moment to zoom in closer at the detail of the data scientist’s skillset. We’ll divide the skills up into two broad categories: technical and nontechnical skills. A well-rounded data scientist who wishes to stand out from the crowd would do well to be able to demonstrate competence in both of these fields.
The most sought-after technical skills for a data scientist
Advanced knowledge of statistics
Data scientists are called upon to apply a range of statistical concepts and methods in their daily work. Those showing a deep knowledge of statistical analysis methods; interpretation of distribution curves; and a firm grasp of probability computation, standard deviation and variance possess top-tier skills that employers are prepared to search far and wide to find.
Mathematical concepts: multivariable analysis and linear algebra
The ability to comprehend and calculate the ‘fitting functions’ necessary for optimal matching between models and data sets has become a pressing priority for business success in the age of Big Data. If this fails, so does the ability of a model to make accurate predictions, making this knowledge of critical value to an enterprise. However, so also is the ability to simplify what would otherwise be mind-bogglingly complex analysis problems that are inherent to ‘high-dimensional’ data – data so laden with variables and large in volume that the number of variables matches or exceeds the number of observations (data points). This simplification process has to be undertaken rigorously to avoid over-simplification, which could yield false or inaccurate results. Data scientists demonstrating aptitudes in dimensionality reduction fit the bill very well here. However, advanced competencies in calculus and algebra are also highly desirable in the ‘mathematics’ skill set. These are the means by which a data scientist can, for example, train an artificial neural network to manage vast volumes of data.
Predictive modeling
Related to both of the above is the ability to draw on data to formulate forecasts and model a range of different outcomes and scenarios depending on what variables (and at what magnitude) are at play. Data scientists who can demonstrate competencies in predictive analysis are therefore at an advantage over rivals who can’t. This is the field that enables data scientists to identify patterns in existing or new data sets and draw on them to develop projections for future occurrences, results or behaviors. Predictive modeling is a particularly versatile (and prized) skill set for data scientists to acquire proficiency in, as it can be applied across multiple different industries, from medical diagnostics to customer analytics to equipment maintenance.
Coding and programming skills
Many data scientists do not possess a degree in computer science and would not describe themselves as experts in coding. However, during their data science training, they do nonetheless acquire a working familiarity with the pragmatics of programming and writing code. Those with demonstrable practical proficiency in the world’s most widely used computer programming language, Python, will again be at an advantage over those who lack it. According to a 2020 survey by Kaggle (a subsidiary of Google), 80% of more than 2,000 respondents holding data scientist roles reported that they used Python. The next most popular language was SQL, with 40% of survey respondents reporting that they used it in their work. Data scientists involved in statistical computing and graphics are also more likely to have acquired competencies in another popular computer language, R, while C and C++ are also widely used. Possessing proficiencies in these programming languages places candidates at another distinct advantage over competitors who lack them.
Machine learning (ML) and deep learning
Although we previously mentioned that more data scientists today are acquiring a working knowledge of AI, most companies seeking their expertise are actually looking more frequently for an ability to implement ML applications. That translates into the ability to train ML algorithms to learn from data sets and discern patterns, irregularities and/or fresh insights that can then be funneled into the construction of new analytical models. Demand is mounting strongly for data scientists with proficiencies in the supervised, unsupervised and reinforcement learning methods that ML rests upon. At a more advanced level, data scientists with knowledge of deep learning – a specialism that uses artificial neural networks to yield highly intricate analytical models – will really stand out from the crowd.
Building and implementing models
The bulk of a data scientist’s time is spent in this field. Constructing effective models entails finding the optimal algorithm to, say, ‘train up’ a supervised machine learning process by feeding the right data through it, or to ‘teach’ unsupervised machine learning processes to identify constellations or patterns. After testing the model to ensure that it generates the desired results (work that’s often done in collaboration with data engineers), data scientists are required to implement or deploy it in a real-world production process. This helps businesses to implement effective business decisions on a continuing basis and, again, is a skill highly prized by business leaders.
Data visualization
Data scientists have the expertise to find their way around and analyze colossal data sets – so-called ‘Big Data’ – that are often laden with a complex array of different types of data. However, they face a problem: their colleagues, including business decision makers who rely on their input to implement evolving plans, don’t possess this intricate knowledge. So, how can they make their findings comprehensible to such a team? Data scientists have overcome this barrier by means of a form of ‘data storytelling’. Having done the hard expert work of identifying patterns and trends, they will often use visual methods to get their points across. Often, they do this through their mastery of data visualization tools such as D3.js or Tableau, but they will also be capable of converting their data-derived findings into graphical form – pie charts, bar charts, line and histogram depictions, or even bubble charts, scatter plots and heat maps.
Presenting extremely complex concepts to laypeople in comprehensible forms such as this is a defining feature of a pragmatic expert, and it’s an indispensable skill for an effective data scientist.
This last point brings us on to another set of competencies that are often termed ‘soft skills’. All are related to that particular point, however: having specialist expertise in an organization is of little value if it can’t be effectively communicated to colleagues who lack the advanced knowledge of a data scientist.
Essential nontechnical skills for effective data scientists
- Business know-how
A large number of organizations consider the data scientist’s role as a business function rather than a purely technical one. It therefore behooves prospective data scientists to understand how the business and industry they work in function, not least because they need to be able to ask the right questions about how they can enhance their business’s success.
- Teamwork and effective communication
“No man is an island”, as the great 17th-century English poet John Donne wrote. This is especially true of a data scientist, who for the most part will be unable to function in isolation: their insights need to be communicated to other members of the team in ways that they can understand and value. Data scientists are often called upon to work with other departments, such as designers, other data pros, clients and executives. This means that they must possess an ability to not only ‘get along’ smoothly with any of these colleagues for collaborative work, but also communicate complex concepts in ways that they can grasp and make use of.
- Problem-solving through analytic and structured thinking
When problems arise in an organization or business, they often need to be solved pronto. While it’s sometimes possible to mull it over at one’s leisure, this is rarely the case. More typically, data scientists are approached for help when an urgent solution is required, even if colleagues are unable to define what that solution should look like. It’s often down to the data scientist to come up with viable options. This requires thinking about the presented problem in a disciplined, structured and analytical way – abilities that their advanced training will have helped instill in them. This entails, for example, exploring the circumstances in which the problem has arisen from all angles, identifying what has caused it and being mindful of the possibility of overlooking something by knowing one’s biases/assumptions and suspending them during the problem-solving process.
- Patience
This isn’t a ‘preachy’ commandment – it’s a desirable ability. Data scientists often find themselves being asked by non-technical colleagues for technical solutions for their projects that they have recently heard of (such as neural networks), but about which they don’t understand and which in fact may be totally inappropriate – or by technical colleagues for things that the business picture simply isn’t in a position to permit. Staying calm in the middle of all this and coming up with pragmatic answers that may not ‘fit the bill’, but that are true and practical is a real art that adds considerable symbolic value to the data scientist’s stature.
Data science isn’t a discipline that can be mastered in a few weeks. However, for those with the aptitudes, and the will and resolve to study it deeply and become properly qualified, it offers a rewarding and endlessly fascinating career.
