Researchers often use coding in order to classify and organize their data. They attach conceptual labels to empirical objects and then organize and interpret them to answer a research question. But coding isn’t required for every data innovation. Here’s an overview of what it means and how it might be used. But is coding always necessary? How can researchers use it in their data innovation work? Let’s discuss some ways in which data innovation can benefit from coding.
Most consequential code helps to remove friction. That is, software that makes something easier to use results in more use. Internet Relay Chat, for example, was created with code in 1988. Now, real-time text chat is everywhere. It’s used in workplace communication tools and video platforms such as Twitch. Ultimately, code can be transformative. If data scientists want to create a better world, coding can help.
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Coding is important to data innovation. In fact, the role of data scientist relies on coding. Although most data scientists aren’t computer scientists, it’s important to learn how to code before applying for jobs in the field. Coding skills will become more important as the field grows. With the rapid expansion of data innovation, new job opportunities are opening up in companies of all sizes and fields.
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