Helping learners make data-driven decisions
Imagine you’re starting a new job at a tech company, and you’re excited to be joining a team of innovative thinkers who are on the cutting edge of technology. On your first day, you’re handed a data set and asked to analyse it to provide insights to help your team make strategic decisions. Your heart sinks as you realise that your years of studying didn’t quite prepare you for this task.
This scenario is becoming increasingly common in the modern workplace, where data literacy is now a fundamental skill for success. According to the Department for Digital Culture, Media and Sport (DCMS), data analysis will be one of the most in-demand skills over the next five years.
As the UK Govt states; ‘while not every worker needs to become a data scientist, everyone will need a basic level of data literacy to operate and thrive in increasingly ‘data-rich’ environments’. In other words, if learners want to be competitive in today’s job market, they need to understand how to capture, analyse, interpret and visualise data.
Embedding data literacy into the curriculum
Machine learning and Artificial Intelligence (AI) can be incredibly powerful tools for analysing and interpreting large and complex data sets. By using sophisticated algorithms, they can identify patterns and relationships within the data, helping learners uncover insights that might be difficult or impossible to see with the naked eye.
We need to be facilitating opportunities for learners to use such tools to gain hands-experience with data. Whether this is through more complex tools such as Tableau (who offer free resources and training to higher education providers) and Pandas (Python) or more accessible tools such as Excel and Google Sheets.
There are plenty of ways to embed data literacy into a variety of subjects, even those not STEM related. English students can use machine learning tools such as Voyant to compare themes within literature. Business students can use real-time data from the Office for National Statistics about organisations to conduct competitor analysis. Healthcare students can use real-world data from the local population to identify public health trends. Social sciences students can use sentiment analysis tools such as Mozdeh to investigate social media trends, and changes in public opinion.
Organisations such as the Bright Initiative and the Data Literacy Project also work in partnership with many education providers across the UK, with the aim of improving learners’ data literacy skills.
Preparing learners for the future workplace
However, data literacy is just the tip of the iceberg. Creativity, inquiry, problem solving, and adaptability are also essential skills for the future of work.
Employers are looking for people who can think critically, ask the right questions, and come up with innovative solutions to complex problems. They want employees who can adapt to new technologies and work processes quickly and effectively. These skills are necessary even with the rise of machine learning and artificial intelligence.
Join in the conversation
If you’re interested in the skills a future employable learner might need, join us at our next ‘developing learners’ employability skills’ workshop on: