INSIGHTS

Upskilling and Reskilling to Overcome Data Science Skills Shortage

7 min read ·

Nov 21

Shaun Dippnall

VP, Enterprise AI

Across the world, there is a growing need for more data science skills in businesses. The problem will only worsen due to emerging technologies and continued advancements in Industry 4.0. The nature of jobs is changing, and the effects will spread far and wide.

The result is already playing out in countries like South Africa, where unemployment is critical. According to a PWC report, eight out of ten unemployed adults have some secondary schooling, including matric, and one in ten unemployed people in South Africa has a tertiary qualification.

The study highlights a skill shortage in the broad sphere of data science. Four of the top ten ‘future jobs’ in South Africa are specifically tech-oriented: cloud engineers, data scientists, development and IT operations engineers, and data engineers.

The report also found that the critical need for more specific skills is slowing economic growth. It concluded that data science upskilling for workers could be one of the most important drivers of GDP and job growth. It states, “Companies can help turn this crisis into an opportunity by upskilling their workers. Upskilling and workforce investment are a core business principle.”

The situation in South Africa is not isolated. The demand for data science skills is impacting many countries. Work candidates with secondary education are not adequately skilled for the modern workforce. Companies must find reskilling and upskilling data science programs to develop these skills among their new and existing employees and close the data analytics gap.

What's the Difference Between Reskilling and Upskilling?

Data science reskilling and upskilling strategies provide employees with new skills. And both methods can help companies ease the growing data science skills gap.

The difference is the level of skills acquired. Upskilling in data science involves learning new skills to become more proficient in a current role. Reskilling in data science is developing new skills to transition to a different role. For example, a marketing team can upskill and learn generative AI to become more productive in their existing workflows. Likewise, an employee can reskill by learning Python or SQL to become a Data Scientist and help the company gain valuable insights from its data.

While reskilling requires more training and elevates the technical skills of the employee, everyone in the organization, from the first-year employee to the CEO, will need upskilling in data science. Data and AI will have a significant impact on organizations. Even the C-suite will need a baseline understanding of data science to lead a company into the data-driven future.

The report also found that the critical need for more specific skills is slowing economic growth. It concluded that data science upskilling for workers could be one of the most important drivers of GDP and job growth.

Why Data Upskilling and Reskilling is Critical

The argument for providing data science upskilling and reskilling opportunities within an organization has many facets, including the previously mentioned impact on economic growth.

From a company’s perspective, the benefits can mean improved market competitiveness, higher employee retention, and enhanced innovation.

Given the depth of the talent shortage, the data science skills gap can only be closed with data upskilling and reskilling programs.

According to Shaun Dippnall, co-founder of Sand Academy and Head of Enterprise AI, in 2017, the academy began training young data scientists in a 12-month learning program.

“This program continues successfully, but as the need for data science skills has grown, our focus has broadened to include data science upskilling and reskilling for people at all levels of the business,” Dippnall says.

Further, Dippnall states, “Because data science is a journey and not a destination, executives need to look at the twin focus areas of upskilling and reskilling. These may sound similar, but they are not. The company must have a continuous, planned data science reskilling and upskilling strategy to help close the data science skills gap.”

What is Data Science Upskilling?

Data science upskilling gives employees new skills to expand their existing roles. It could be as simple as teaching an employee how to use pivot tables or writing basic macros to speed up data analysis.

As AI and data analytics impact more positions, getting existing employees comfortable with and knowledgeable about using basic data science skills is essential since data science will positively impact job effectiveness.

Companies can upskill many in the workforce by providing access to online learning platforms or task corporate training departments to upskill the employees.

Corporate management teams, including the C-suite, are essential to the upskilling data science strategy. The management team must understand AI’s potential to lead the organization in a data-focused strategy.

Sand Academy has programs to teach new skills at all levels and highly curated training designed for management, which includes:

  • Strategic briefings to senior executives
  • Hands-on interventions in all areas of management: line managers, production managers, accountants, logistics, marketing, and HR personnel
  • Building a pipeline of external and internal talent to feed into the business

What is Data Science Reskilling?

Reskilling in data science gives employees new skills to improve their efficiency in existing roles or to transition to more advanced, hard-to-fill roles. As technology, specifically data science, begins to permeate the organization, data science reskilling will be required.

Data science, combined with AI automation, will make insights available to more employees, giving them the information they need to make better decisions and work at a higher level of proficiency. However, to correctly use the data, they must use new tools, which requires reskilling.

Research into reskilling for data science across an economy shows that improving the quality of and access to education and training requires a change in culture and behavior. Today’s organizations must shift to lifelong learning pathways, grow in digital education, and adopt new funding models for higher education to remain competitive in tomorrow’s economy.

Data science reskilling “requires everyone within the business to understand that learning data science is a process, not a destination. Technology is changing constantly, and employees need to adopt a hunger to learn, which results in constantly absorbing new skills,” says Sand Academy’s Dippnall.

The demand for data science skills is impacting many countries. Work candidates with secondary education are not adequately skilled for the modern workforce.

Benefits of Upskilling and Reskilling in Data Science

Upskilling in data science benefits companies and employees alike. The main benefits for companies are retaining valuable industry and market knowledge, customizing skills training to match the companies’ needs, and gaining a higher level of competitive advantage.

Employees are worried about losing their jobs to AI and automation. The irony is that companies will need more people with data science skills. Data science reskilling and upskilling programs can improve confidence and job satisfaction and give employees more opportunities to move up.

The chart below features the most common benefits for each.

Benefits of Upskilling and Reskilling in Data Science

For Companies

For Employees

It helps close the skills gap

Addresses the concerns many employees have that they will lose their jobs

Keep core teams the same

Better job security

Can customize training programs to design the workforce you need

Increases job satisfaction

Benefit from automation, leveraging advanced insights

More growth opportunities

More agility and competitiveness to respond to new changes in the market

Can lead to upward mobility

Keep earned department and industry knowledge in-house

Improved collaboration

Gain a better understanding of the customer - can build more meaningful products, services, and experiences

Can move into hard-to-fill, better-paying roles

Save time and money in recruiting and onboarding by reducing turnover

Opens old roles for others to move up (roles that are easier to fill)

Waiting for qualified data science candidates may reduce the competitive edge

Who Should be Upskilling and Reskilling?

The transition to Industry 4.0 will likely impact at least half of all jobs. So, who should be upskilling and reskilling in data science? The answer is every company.

Start with a company-wide assessment of existing skills. Survey every employee to collect current abilities. Include questions to determine which employees are more data—or AI-oriented.

Next, the management of each department needs to map any skills required for planned software upgrades or new software.

Once a company’s skills gap is determined, create data science reskilling and upskilling plans with mentorship programs in mind. Consider pairing newer employees with mid-tier employees to develop a buddy system. Each pair should benefit by raising the company’s technology and industry/market IQ.

The HR team can help identify a method for learning these new skills. There are various ways to upskill employees, from online learning modules to in-house training programs to formal external learning.

A growing strategy is to have industry AI talent, and experts curate training for each learning cohort in the organization. Sand Academy provides training programs for first-year employees to the CEO. Each group has unique learning needs, and having a trusted training partner who understands the training nuances for each group can help everyone adapt to organizational and industry changes.

When is it Time to Upskill and Reskill?

The ideal time to upskill and reskill is before a need is immediate. Get started when the need is a growing concern or an upcoming pain point for the organization. Date science upskilling and reskilling for your team is essential to helping ensure productivity continues without delay.

Ideally, data science upskilling and reskilling should be a part of everyone’s employee growth plan to help build a pipeline of available talent for the organization and future development. While new technologies may not be impacting your organization now or may only affect a few departments, take this time to plan for future changes.

GenAI is an excellent place to start upskilling. Many marketing and sales teams already use generative AI, which can dramatically impact workflows. But GenAI is nuanced. Even with its user-friendly prompts, it requires training to use effectively. While upskilling for GenAI, use the experience to guide new teams and discover new workflows where GenAI can add more value.

A starting point for reskilling is training an employee in SQL or Python and then pairing them with an existing Data Architect. This team can work together to begin basic projects for data analysis.

Today’s organizations must shift to lifelong learning pathways, grow in digital education, and adopt new funding models for higher education to remain competitive in tomorrow’s economy.

Which Data Science Skills are Most in Demand?

The impact of data science on existing roles will vary. As mentioned, learning to use generative AI to improve efficiency is unlike learning Python (even though Python is considered one of the easiest languages to learn).

Everyone in the company does not need to be a programmer or fluent in software engineering. But for those reskilling in data science, rest assured that it won’t take a math degree to learn some of the most in-demand languages.

Popular programming languages typically have an English-language interface and basic syntax rules. Gradually, generative AI will make using these languages even more accessible.

The most in-demand skills include data engineering, technical, and soft skills. The list of the most needed data engineering skills changes yearly, but Python and SQL consistently make the top ten as they help companies analyze and mine their data for insights.

A budding new entrant is NoSQL. This language analyzes unstructured data, which is the fastest-growing type of data. Unstructured data includes video, images, sound, etc.

The Top In-demand Skills for 2024

Data Engineering

Technical Skills

Soft Skills

Python, SQL, TypeScript, JavaScript, Swift, Rust, Kotlin, GoLang, Java, C++, C#, Ruby, PHP, and NoSQL

Data visualization skills, machine learning and AI, deep learning, considerable data skills, cloud skills, cybersecurity, data analysis

Business acumen, communication skills (particularly negotiation skills), data ethics skills, strategic thinking, presentation skills, critical thinking, mentoring, emotional intelligence (EQ), innovation, resilience

The list clarifies that future jobs will require a different way of thinking and working. Historically, jobs were more siloed, each employee being a single contributor. Industry 4.0 will require openness, collaboration and the ability to synthesize more information.

Upskill and Reskill with Sand Academy

Sand Academy approaches data science training, upskilling, and reskilling at several levels and with specific interventions. Its 12-month data science learnership covers Python, regression, Power BI, statistics, mathematics, communication, problem-solving, Pandas, and Scikit-learn. The learning program integrates into the student’s regular job, and the skills taught are real-world oriented, focusing on company-specific problems.

There are also part-time courses and so-called boot camps available. The part-time courses are primarily technical—including coding and visualization, databases, cloud, and deployment—but they also include problem-solving, working in teams, and solving real-world problems on the job.

Boot camps offer a range of specialized skills programs at different organizational levels. These courses last from one to three days and upskill or reskill employees, including data scientists, data users, and members of management. These programs help companies close the data science skills gap.

Sand Academy’s Dippnall concludes: “With both the year-long course and the boot camp courses available, learning and development managers can work with Sand Academy to advance their company’s in-house data capabilities and become part of the worldwide trend to build skills that will cope with the Fourth Industrial Revolution. The strategy should focus on data science reskilling, upskilling, and building bespoke courses to address each company’s specific needs.”

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