Photo by the editor
Every day there are recent breakthroughs in the field of Data Science and it seems vital for our careers. With so much going on, it’s quite clear that we need to be on top of our game every day, as complex as it is.
Of course, we can learn all these skills ourselves. but it’s demanding to understand everything that’s coming out and what to focus on. This is where communities shine, helping us gain recent knowledge imperative to our careers.
Many smaller and larger data science communities could support improve our careers. However, if you only have time to spend in five different communities, these are the ones you should focus on.
Let’s start.
Let’s start with what seems ordinary: Reddit.
Yes, Reddit is a social media forum famed for memes and random discussions about life. However, it is still a social media platform with a bulletin board structure that leads to a lot of casual discussions. One of them is data science.
Reddit is truly a dynamic place for data science enthusiasts and professionals alike. It is vital to find specific communities on Reddit that could make learning easier for us.
For this purpose, I recommend joining r/datascience a community that boasts 1.4 million members and hosts recent discussions every day. There was a discussion on topics related to data analytics almost every day. From basic questions to advanced career moves, the community is lively and eager to discuss with everyone.
Additionally, r/machine learning is a great community to join if you want to discuss specific machine learning topics with over 2.9 million other members.
The next community you should join is Kaggle.
I’m sure many of you who have started learning data science have heard of this. However, if you’ve never heard of Kaggle, it’s a platform for sharing and publishing datasets, exploring and building machine learning models using notebook, and sharing all of our data work. It is also a hub for data analytics competitions as many companies host online competitions on this platform.
Kaggle’s purpose is to make our data science work accessible to the public, so it has become a central place for many data scientists to communicate. I have already received so much support from the community there in my personal and professional work that I consider it to be one of the best communities in the world.
This is because a lot of useful information and groundbreaking discoveries have been implemented there, which is a great way to acquire recent skills useful in career development. Moreover, many professionals and experts contribute a lot to the community for free.
The Kaggle community is open to every level of your data career. Whether you’re a budding pro or a competition champion, the Kaggle community is here to support.
For some people, LinkedIn is a place to look for a recent job or an educational platform. However, social media is still at the core.
LinkedIn allows its users to create social groups where like-minded people can post and share their thoughts. One of the largest LinkedIn Data Science social groups is Data Science Community (Moderated) by Robbert van Vlijmen, which has over 500,000 members.
Members often share useful learning materials and provide a place where you can ask any question you have, regardless of your professional level. So don’t feel pressured if you think the discussion seems too complex to understand.
Another LinkedIn group I recommended you join is KDnuggets Data Science and Machine Learning (moderated). With over 150,000 members inside, there are daily posts from them to support you learn, including the latest article from KDNuggets that you can discuss inside.
KDNuggets LinkedIn group community (author’s photo)
Several other communities with vast memberships that are still related to data science, including:
The DataTalks.Club is a community of data enthusiasts created by Alexei Grigoriev.
It is a community dedicated to discussing all data-related topics, from data analytics, machine learning and data engineering to data science. Whatever questions you have about your data, the community is here to answer them.
However, the best part of this community is that you can discuss any career-related issues, such as deciding on your next step or what to learn. If you’re looking to engage with the community, joining the DataTalks Slack channel is a great way to continue the discussion.
The community also hosts numerous events that you can join. From petite one-off events to free training camps, you can join the community. These free boot camps are certainly famed for providing free, company-level skills that you might have to pay a lot of money for elsewhere.
Additionally, there are podcasts and book discussions from which you can learn something. The point is that this community is actively trying to make you a better person in your data career.
The last community I recommend for developing your career is MLOps Community.
MLOps Community (author’s photo)
So listen to me. The community does not necessarily have to be specific to data science, as MLOps is part of data science. However, a successful data analytics project is one where we can deliver ongoing value to the company, and MLOps delivers this.
That’s why I recommend the MLOps community as a place that can support you in your future career. I also state this because MLOps is becoming a skill that many people are looking for in data science roles.
The community, especially Slack, is lively, with many conversations about MLOps and sometimes general discussion. Nevertheless, I gained a lot of insight there and you should too.
The Data Science community plays a significant role in developing our careers. By interacting and learning from others, we can understand many recent things that we could not achieve before. There are many data science communities, but here are five communities worth joining:
- Kaggle
- Data conversations
- MLOps Community
I hope it will support! Share your thoughts about the communities listed here and add your comment below.
Cornelius Yudha Vijaya is an assistant data science manager and data writer. Working full time at Allianz Indonesia, he loves sharing Python tips and data through social media and writing media. Cornellius writes on a variety of topics related to artificial intelligence and machine learning.