The ability to understand and communicate about data is an increasingly important skill for the 21st-century citizen, for three reasons. First, data science and AI are affecting many industries globally, from healthcare and government to agriculture and finance. Second, much of the news is reported through the lenses of data and predictive models. And third, so much of our personal data is being used to define how we interact with the world.
Your Data Literacy Depends on Understanding the Types of Data and How They’re Captured
Nontechnical people need to speak the language too.
October 23, 2018
Summary.
The data-related concepts non-technical people need to understand fall into five buckets: (i) data generation, collection and storage, (ii) what data looks and feels like to data scientists and analysts, (iii) statistics intuition and common statistical pitfalls, (iv) model building, machine learning and AI, and (v) the ethics of data, big and small. The first two are easily overlooked. The capture of data depends on the use case. Data scientists mostly encounter data in one of three forms: (i) tabular data (that is, data in a table, like a spreadsheet), (ii) image data or (iii) unstructured data, such as natural language text or html code, which makes up the majority of the world’s data.
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Excel in a world that's being continually transformed by technology.