Meaningful, insightful and impactful research depends on a researcher’s data literacy: the ability to understand, work with, analyse, reflect on, interpret and communicate data. Data literacy is increasingly important for researchers in a field characterised by fast-paced change. Doctoral programmes need to prioritise developing researchers’ skills, which will in turn equip them with the ability to think more critically and make impactful decisions based on evidence, not hunches.
Integrating courses in data literacy
Including data literacy-focused courses in doctoral curricula is a crucial first step in raising data literacy levels among doctoral students. The focus of these courses needs to go beyond statistical literacy and enhance understanding of and the ability to leverage the data ecosystem. This includes:
- The characteristics of data
- Data gathering
- Data governance
- Ethical considerations with datasets
- Data wrangling
- Reading and interpreting data through data analysis with an emphasis on drawing meaningful conclusions from it
- Communication of the data through effective academic writing
- Data visualisation techniques
- Incorporation of media in data communication
- Data team management
- Hands-on workshops
Researchers need hands-on experience to develop confidence in data analysis. Running hands-on workshops and training sessions that centre around real-world data analysis scenarios can help doctoral students transform theoretical information into practical applications. During these sessions, tutors can lead them through practical exercises such as data cleaning, data science modelling, data visualisation and using Structured Query Language (SQL) for processing information using real datasets.
Guest lectures by industry experts
Invite prominent data scientists within and beyond academia to deliver guest lectures that shed light on trends and practical data science applications. Exposure to diverse perspectives and professional expertise equips students with the knowledge they need to navigate the complexities of data science.
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Peer learning
Academic communities that encourage and facilitate knowledge-sharing are ideal for fostering data literacy. Encourage researchers to engage with their peers by establishing data science clubs, discussion forums or peer mentorship programmes that promote a culture of collaborative learning.
Encourage collaboration across disciplines
Creating a comprehensive strategy for improving data literacy requires dismantling barriers between academic disciplines. Encouraging multidisciplinary collaboration exposes scholars to diverse perspectives and methodologies, fostering a richer understanding of data analysis techniques. By working alongside researchers from other departments, scholars gain the skills required to bridge the gap between their own fields and the world of data science.
Building a data literacy mindset
Building a data literacy mindset goes beyond simply empowering students to acquire data skills. It’s also about cultivating a way of thinking that revolves around critically evaluating and effectively using information. We must make an effort to embed this into the institution’s culture by encouraging curiosity and scepticism, problem-solving, storytelling and continuous learning.
Tech update platforms
Data science is constantly evolving, with new tools and techniques emerging all the time. To keep up with these developments, institutions can establish forums where scholars can stay informed and encourage each other to improve their data skills. Regular webinars covering trends, new technologies and best practices in data analysis will also help students to stay up to date.
Data empowerment hubs
Universities must become data empowerment hubs; researchers must have access to appropriate resources and tools to conduct effective data analysis. By providing access to modern data analysis tools, cloud computing resources, vetted dataset repositories and software subscriptions, institutions equip researchers to explore the ever-evolving data science landscape. Removing access barriers empowers researchers to investigate a wider range of tools and apply the most suitable ones to their specific needs.
Continuous feedback and improvement
Regular assessments help measure progress and identify areas for improvement. These assessments should go beyond testing theoretical knowledge and require students to demonstrate practical data skills. Personalise feedback to pinpoint weaknesses and empower students to refine their data literacy skills throughout their doctoral journeys. This continuous feedback loop is essential for building strong data literacy.
Emphasise data’s moral implications
For students to become fully data literate, they must understand and apply ethical principles throughout the research process. Universities must insist that researchers adhere to ethical standards to foster a sense of ownership and accountability, ensuring both credible academic research and ethical data practices.
Career advancement possibilities
An essential component of improving data literacy is providing researchers with support for their professional development. Institutions should encourage scholars to attend and participate in external data science-related conferences, workshops and seminars, data competitions, collaborative research projects, hackathons and other similar events in preparation for a data-driven research environment.
A truly data-literate PhD graduate emerges from a programme that seamlessly blends theoretical knowledge with hands-on experience, teamwork and ethical considerations. This holistic approach fosters not just research proficiency, but also critical thinkers who can adapt to the evolving needs of academia and industry. By empowering students with data literacy, universities cultivate the next generation of researchers poised to make impactful contributions across their disciplines.
Vasudha Devi is deputy director of the Centre for Doctoral Studies at Manipal Academy of Higher Education.
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