{"id":10109,"date":"2020-12-17T08:20:39","date_gmt":"2020-12-17T16:20:39","guid":{"rendered":"https:\/\/www.springboard.com\/?p=10109"},"modified":"2025-01-27T03:44:46","modified_gmt":"2025-01-27T11:44:46","slug":"data-science-communities","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/data-science-communities\/","title":{"rendered":"9 Data Science Communities to Join in 2025"},"content":{"rendered":"\n

Data science is a great profession<\/span>\u2014but it can also be a lonely one. For many newly qualified data scientists, the first few years out of college are a real challenge. Without the camaraderie and friendship provided by your peers, it can quickly begin to feel isolating. <\/span><\/p>\n\n\n\n

However, data science is actually an inherently social profession. Part of a data scientist’s role is to work alongside internal and external stakeholders to deliver key insights; outside of that, many data scientists like to stay connected to their peers working in similar roles as a way to discover new ideas and methodologies. <\/span><\/p>\n\n\n\n

One of the best ways data scientists network with each other is through data science communities. In this post, we\u2019ll run through the most popular data science communities and show you how you can get involved.<\/span><\/p>\n\n\n\n

1. Kaggle<\/a><\/b><\/h3>\n\n\n\n

Technically, Kaggle is a coding tool rather than a community per se, but the Google-owned software is so popular among data scientists<\/a> that it now boasts one of the largest data science communities in the world.<\/span><\/p>\n\n\n\n

Kaggle has mainly been built as a tool to evaluate and assemble teams across competitions. It allows you to find and <\/span>publish data sets<\/span><\/a>, explore and build models in a web-based environment, and share your work with other scientists and engineers. You can even enter competitions directly from the software, which offers over 50,000 public <\/span>datasets<\/span><\/a> and 400,000 public <\/span>notebooks<\/span><\/a> to address even the most complex challenges.<\/span><\/p>\n\n\n\n

Another extremely useful feature of Kaggle is that it provides free access to NVidia K80 GPUs in kernels. This benchmark shows that enabling a GPU to your Kernel results in a 12.5X speedup during the training of a deep learning model. There are loads of great resources on how to start using the Kaggle in this way, including <\/span>this guide<\/span><\/a>.<\/span><\/p>\n\n\n\n

Best of all, the Kaggle community now has more than 3 million active members who can use Kaggle to share their work and expertise.<\/span><\/p>\n\n\n