Emily Glassberg Sands, Director of Data Science at Coursera

As we move into the next decade, talent will underpin the UK’s efforts to maintain its position as a world-leader in innovation 

The UK’s AI industry is set to grow exponentially over the next few years, contributing £232 billion to the economy by 2030.

But even with the country poised to become the innovation centre of Europe, progress is hindered by the ongoing skills crisis.

In fact, the UK could be missing out on as much as £63 billion a year because companies are not finding workers with the necessary critical digital skills.

There is evidence that the tides may be turning.

Results from Coursera’s database of 1.3 million UK-based learners show that AI-related courses were the most popular studied in the UK this year, with Stanford University’s online Machine Learning course topping the charts, closely followed by courses such as AI for EveryoneIntroduction to TensorFlow, and Programming for Everybody.

Despite the promising trend of Brits studying technical subjects online, more must be done to ensure everyone — regardless of age, background, or occupation — has the opportunity to develop fundamental digital skills.

A widening gap

This year, the UK government announced investments of £1 billion in the AI sector, including funding for 1,000 PhDs and training for 8,000 secondary school teachers in computer science. This is a clear commitment to nurturing digital competencies from secondary school to university, but what about businesses desperate for digitally literate workers right now?

Estimates suggest that Europe will need 346,000 more people trained in data science by next year.

Thanks to digital transformation, demand for data specialists is being felt across every sector, not just technology companies.

As the amount of data in the world increases exponentially (IDC forecasts by 2025 the volume of data worldwide will increase by 61%), the demand for data specialists will continue to grow in tandem. In addition to dedicated data scientists, engineers and analysts, companies will also need employees across the business to think — at least a bit — like data scientists, translating data into actionable insights.

Talent to support innovation 

Coursera’s end of year findings indicate that people are keen to learn, willing to enroll in online courses on their own to learn new skills. Similarly, PwC found that 54% of UK adults say they’re ready to learn new skills or completely retain to improve their employability. To bridge the digital skills gap, companies must offer those employees who want to upskill or reskill the opportunity to do so.

Given the rapid rate of technological developments, learning can no longer be confined to the beginning of our working lives. The onus is on businesses to build a workplace culture where learning is celebrated and workers have the time and headspace for self-development.

As we move into the next decade, talent will underpin the UK’s efforts to maintain its position as a world-leader in innovation. Recognizing this, businesses will hold a major responsibility ensuring individuals at every career stage can gain valuable digital skills.

Emily Glassberg Sands, Director of Data Science at Coursera

Emily Glassberg Sands is the Senior Director of Data Science at Coursera, the world’s leading platform for higher education. More than 46 million learners come to Coursera to advance their skills in courses, Specializations, certificates, and degree programs offered by university and industry partners including Yale, Stanford, and Google.

Emily’s team builds the statistical models and machine learning algorithms that power content discovery and scale an engaging and personalized learning experience; leads the measurement, experimentation, and inference that informs Coursera’s product and business strategy; and develops the analytical products and direct data access for the company’s university partners and enterprise customers. She holds a Ph.D. in Economics from Harvard and a B.A. from Princeton.

Her academic research blends experimentation, econometrics, and machine learning to understand labour markets and consumer decision-making, and has been featured in the popular press including the New York Times and National Public Radio.

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