Fast track your career by upskilling to data science and data engineering

Data exists in vast quantities in every business. Statista for example, estimates the total amount of data created, captured, copied, and consumed in the world is forecast to increase rapidly, from 59 zettabytes in 2020 to 149 zettabytes in 2024.  This is compared to just 2 zettabytes in 2010. With this explosion in the amount of data generated, all organisations including financial services, logistics, retailing, healthcare as well as Government, are looking to leverage data to drive efficiencies, deliver customer insights and make better decisions.

By making data science integral to their daily operations, organisations have benefitted from a wave of innovation and growth. Typically, however, an estimated 80% of corporate data is unstructured and needs predictive analytic tools to gain insights from it. With that comes the need to hire people with the right skills to collect, read and analyse data.

How important are data scientists to the future of business?

Data is the new oil in terms of driving business competitiveness and innovation.  As organisations increasingly rely on information from data for their decision making, the role of the data scientist will become ever more important. However, the value of the data scientist can only be optimised if businesses gear themselves internally to the data science journey.

This means identifying the specific areas and opportunities in the organisation where data science can add value, then finding the skills and technical resources needed to cope with the technicalities. Usually, existing internal resources are insufficient, so external resources should be called in if required.  

It’s also important that company executives understand the process. This includes clearly articulating the particular problem areas in the business, as well as gathering, cleaning, wrangling and preparing the data. Analysing the data comes next, followed by visualising results in interactive and intuitive ways and communicating findings to non-technical stakeholders who need to act on the insights.

Building a data science team with a diverse set of skills is crucial too. This may comprise some or all the following roles: analytics translator, data scientist, data engineer, data analyst, business intelligence developer and software developer.

Once there is a competent data team in the organisation, it needs to fit comfortably into the business’s structure. Ideally, a team should operate as a “Centre of Excellence” within the organisation, rather than decentralising it across departments or simply placing it with IT.

Skills conversions for roles in data science and AI?  

There are a variety of roles within data science – data engineering, data analyst, data visualisation expert, machine learning expert, and more. This can be confusing especially when the sector is changing as quickly as it is. My advice would be to talk to various people in the field to get a better idea of where it is you want to fit and how your existing skillsets and qualifications would best suit the role. Once you understand the role and its requirements you are better positioned to find an appropriate course.

When choosing a course, make sure that it offers a combination of self-study, teamwork as well as some real-world problem-solving projects – preferably ones that can be directly applicable to your place of work. The latter is particularly important to someone wanting to upskill so that they can ensure that what they are being taught can be directly applied to their place of work and so enhance their marketability.

A career in the rapidly expanding field of data science is becoming increasingly accessible thanks to the availability of online courses that give students the various skills necessary to solve real-world business problems, the gap between the demand for these skills by businesses and the supply of competent data scientists is being addressed.

But there is still a long way to go. Data from QuantHub indicates that internationally there was a shortage of about 250,000 data science professionals in 2020.  A report by the World Economic Forum listed 20 specific job skills where demand would be the greatest in the next 5 years. The top 3 were data analysts and scientists, AI and machine learning specialists and big data specialists.  Also on the list were digital transformation specialists, software and application developers and Internet of Things specialists. 

Power of online learning

This is where the power of the online course comes into play. Previously, candidates wanting to pursue a data science career had to take a full-time course or attend a university.  Now that has changed with the advent of online courses designed and presented by world-class specialists in the field. Put simply, a well-constructed, up-to-date short or long course can radically change the career path of an ambitious, hard-working person, no matter what their background may be.

It is becoming clear that successful data science students come from a variety of backgrounds, not only mathematics and statistics.  Graduates from the arts, sciences and humanities are now successfully switching careers after taking a full-time or long or short online course.

A clear advantage of an online course is the ability of the student to study in non-working hours, meaning that a regular job need not be interrupted.  Typical courses need about 10 hours of work per week for durations ranging from 4 to 12 months, depending on the depth of skills being taught.

Critically, individuals considering a career switch or wanting to increase the depth of their data science skills need to carefully consider the options, particularly in terms of the institutions offering the course.  The track record of the institution needs to be evaluated in terms of its practical application and the ability of its courses to solve real-world business problems.  In a fast-changing environment, students need to be confident that learning platforms are innovative and designed by world class scientist facilitators.  Finally, the verifiable history of the institution in terms of successful placement of its graduates and some idea of their starting salaries should be investigated up front.

Like any career decision, though, the responsibility ultimately rests with the person.  No amount of training alone can ensure success.  Rather it is the traits of curiosity, determination, willingness to learn and desire for optimisation which makes for a top rate data scientist.  These soft skills, together with a basic aptitude in mathematics and given the right coaching, can transform a candidate into an exceptional data scientist in a relatively short period of time.

The final question to be answered by anyone contemplating a move into data science is this: “what have you got to lose?”

By Shaun Dippnall, founder and CEO of EXPLORE Group