Regrettably, the image of the reclusive, introverted programmer still exists. There is still some bias towards Data Community software engineers, data analysts, and information technology specialists, which dates back to the 1980s when personal computers became more and more popular. Despite the fact that these industries may foster some of the most productive cultures of cooperation and teamwork, some people still have the misconception that programmers only work alone.
These persistent misconceptions prevent ideas like data and community from being thought of together. Stated differently, data communities were once considered contradictory. But with the advancements in computing technology, the emergence of big data, and the pervasiveness of data applications in our daily lives, it is not surprising that new data communities are popping up all over the internet and across the globe. Check out the Data science course online to learn more.
For instance, in order to create data community building, researchers carried out a study in 2017 at the Association for Information Science and Technology. The Body Listening Project, described as “a participatory platform in which participants were invited to engage collectively in the building of a public repository,” was encouraged to be used by participants in addition to others.
Users of the platform were able to discuss their experiences regarding how they listen to and respond to their bodies. In addition to providing people with the opportunity to provide that information, researchers collected and organized a set of data that provided insights into how individuals might enhance their own health management. In the end, all those engaged created a community “that was molded by individuals from numerous diverse backgrounds.”
This example shows how wide and broad the concept of data community development can be. Although the project these data scientists constructed eventually centered on health and cooperation, their original concentration was on the gathering, analysis, and reporting of various data sets. Data community development is a collaborative strategy that facilitates connections and collaboration between data science experts and beginners. Many have started to view the possibilities for data communities in a different way as an interesting and, to be honest, fresh aspect of the data science field.
What Is Data Community Building?
It’s no secret that the business sector still heavily relies on data-driven decision making. Leaders in all sizes of enterprises typically rely on and implement strategies that are informed by data analysis. “Data plays an important role in practically all industries,” according to Ravit Jain, a full-fledged community builder and advocate, “because we use it to create reports that ultimately drive business and promote greater efficiency.” The increasing reliance on data science in various sectors and businesses has made it possible for more people than ever to become part of various communities that deal with data both conceptually and practically.
Through his efforts and communications, Ravit hopes to make clear just how welcoming and inclusive data community development can be. He states that the data community “is something which isn’t so tricky” in particular. Many believe it is challenging to venture out and establish a community of one’s own. If you are interested in anything, I would think it’s not that hard.
The majority of people often join the data science community for various reasons. Any type of professional endeavor or hobby, whether driven by curiosity, professional motivation, or even amateur interest, can benefit from and be maximized through the integration of data practices.
According to Ravit, anyone with any degree of expertise or background can join the data science community. Community building starts where your interest lies.” “I’m positive that in order to give individuals the chance to acquire efficient data science techniques, I need to reach out to many populations. It involves using various Slack channels, perusing social media networks, and going to conferences.” According to Ravit, these kinds of activities “play a very important role in helping newcomers meet people.” You are picking up knowledge from various leaders or industry experts in that field. You are now a member of the data community as a result.
Furthermore, Ravit has noted that “the data space works more on feedback from different perspectives in the field considering what people think about.” For instance, as Ravit notes, “there are places where you can do this if you have a project and if you wish to get feedback.” He continues by saying that Slack and Discord are great social media channels for encouraging dialogue and input on data science initiatives.
These platforms guarantee that people can connect more directly because of their seamless file sharing, individual and group instant messaging features, and streaming / screen sharing capabilities. The data world has since encouraged “growing in this space where you can interact, attend events, join channels, and join different communities, connect through platforms like LinkedIn and YouTube, and learn from instructors who are helping you.”
Conclusion
To learn more about Data Science communities, check out the Data Science training and placement course.
