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The importance of incorporating data literacy into academic curricula

The importance of incorporating data literacy into academic curricula


The meaning of data literacy is constantly changing and evolving with technological innovation. Photo credit: Getty Images/iStockPhoto

Do we still need data literacy in the age of AI? Yes, we do. Data literacy provides the foundational knowledge needed to navigate data and effectively leverage the capabilities of AI. According to the World Economic Forum's Future of Jobs 2023 Report, the job market for data-related roles is experiencing significant growth, with over 75% of companies planning to adopt big data, cloud computing and AI technologies in the next five years. Digital platforms, apps, e-commerce and digital transactions are ranked as the technologies most likely to be adopted, as most companies plan to incorporate them into their operations.

Application Gaps

Yet, alarmingly, NASSCOM reports that there is a 51% gap between the demand and supply of AI/ML big data analytics technicians in India. Although interest in AI and cybersecurity is high, students often lack hands-on experience and practical knowledge. Bridging the gap between theoretical understanding and real-world application is critical, and educators need to raise awareness of the data literacy skills gap. Here's what educators can do to prepare students for careers that rely on data skills:

Integrating data literacy into the education system requires a multifaceted approach that goes beyond traditional teaching methods. One effective strategy is to incorporate real data into lesson plans, allowing students to work with authentic data sets and gain hands-on experience with data analysis. For example, students can analyze data on climate change to understand its impact on the environment, or explore data on social media usage to explore trends in online behavior.

Seamless Integration

Another effective approach is to seamlessly integrate data literacy into existing subjects such as math, science, and social studies to help students understand the relevance of data analytics in different contexts. For example, in science, students can analyze data on population growth to understand the impact of human activities on the environment. In medical education, students can leverage patient data to create dashboards outlining patient cases, developing a practical understanding of data in their field. This allows students to derive insights from data and conduct meaningful analysis.

Educational institutions need to develop the right industry partnerships to better align student learning with employers' needs, benefiting both the business community and the education sector. Many companies are offering online tools and certification programs that facilitate a structured learning path for students by providing various resources, such as online training and free software, to equip students with the skills required for roles such as business analysts, data architects, data engineers, etc. The success of such programs can be measured not only by the number of students who enroll, but also by the subsequent employment rates and qualification and certification outcomes. Moreover, success can also be measured by the extent to which graduates contribute meaningfully to the workforce and how acquiring new-age skills enhances their professional profile.

These initiatives are being implemented in many universities across the world, including institutions like IITs, IIMs and National Institutes of Technology (NITs) in India, underscoring their commitment to preparing students for a data-driven world. Through strategic partnerships with technology companies and strong academic programs, educational institutions can effectively bridge the gap between classroom learning and industry requirements.

Continuous learning

Educators need to recognize that data literacy is not just a nice-to-have skill, but a necessary skill for preparing students for the future. By analyzing student performance data, educators can identify areas for improvement, effectively allocate resources to address learning gaps, and equip students with the skills they need to advance into new roles emerging in an increasingly digital-first, data-driven world.

AI, and now generative AI, is rapidly dominating the way we work, empowering data like never before. The Data Literacy: Upskilling Evolution report confirms that over 58% of employees believe data literacy is essential to stay relevant. The ability to work effectively with data for generative AI is not something that can be acquired in a day, meaning the meaning of data literacy is constantly changing and evolving with each innovation in technology. But by making learning an intentional and ongoing process, students should have the data skills they need to build a talent pipeline for the data-driven world of work we face.

The author is Senior Academic Program Manager, Qlik Asia Pacific.




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