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Data literacy can be defined in various ways, but it's not merely about data science or predictive analytics. It involves understanding how to transition from raw data to valuable insights within your specific role.
If you're involved in preparing the data, you need a certain set of skills and mindset for its transformation. If you are consuming the data, perhaps interpreting a report someone else has created, comprehension at a high level is required. For instance, if given a statistical report with 95% probability, there's always risk associated as it isn't 100%.
It's essential to remember that although technical skills are important in understanding data, decision-making also requires human and soft skills such as challenging biases and considering diverse perspectives.
I have created two assessments that are referenced in the book Data Literacy in Practice which I co-authored with Angelika Klidas to test knowledge of data literacy. You can take the assessments online here.
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