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By implementing a prescriptive learning approach, organisations can provide a set of diverse resources that
              align with individual learning styles. This approach ensures that there is:
              ●   customised  learning  journeys  tailored  according  to  different  people(  for  example,  different  educational
                  backgrounds) based on individual needs and preferences.
              ●   a variety of learning materials that cater to different learning styles and help in easier grasping of concepts.
              ●   enough leverage or advantage to the learners to progress at their own pace, accommodating their schedules
                  and learning speeds.
              ●   create  an  environment  that  makes  learners  feel  comfortable  and  gain  new  skills  in  an  environment  to
                  supports continuous learning and encourages self-directed exploration.
              ●   each participant can choose the materials and methods that work best for them, leading to more effective
                  learning and greater improvement in data literacy skills over time.

                                             Evaluate

                                             Designing an evaluation metric for the data literacy program involves creating
                                             a structured framework to assess participants’ progress and the effectiveness
                                             of the program overall. It helps to:

                                             ●  improve participants’ overall data literacy skills.
                                             ●   establish clear criteria to measure the success of the data literacy program
                                               and individual participant growth.

              ●   establish a schedule for assessing participant progress to monitor their development over time.

              Data Literacy Framework—An Iterative Process

              This means the development and enhancement of data literacy skills are not static or one-time event. Instead,
              they evolve through continuous cycles of learning, application, and refinement.
                 • Learning
                    ✶ Learning is the initial stage where individuals acquire new knowledge and skills related to data literacy.
                    ✶  Individuals engage in various learning activities, such as formal training sessions, online courses, reading
                   materials, and hands-on workshops to gain insights into data concepts, tools, and methodologies.
                 • Application
                    ✶  Application involves putting acquired knowledge and skills into practice in real-world contexts.
                    ✶  Individuals apply what they have learned to analyse real datasets, solve data-related problems, and make
                   informed decisions.
                    ✶  They are engaged in data projects, experiments, or simulations to gain practical experience and develop a
                   deeper understanding of data concepts.
                 • Refinement
                    ✶  Refinement focuses on reflecting the past experiences, identifying areas for improvement, and enhancing
                   data literacy skills over time.
                    ✶ Feedback from peers, mentors, supervisors, and outcomes of data-related activities informs the refinement
                   process, guiding individuals to adjust their practices accordingly.

                       Data Security and Privacy


              The terms data security and data privacy are often used interchangeably, but they mean different things. Data privacy
              determines who can access the data, while data security involves tools and policies to restrict access to the data.

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