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How A Data Scientist Can Effectively Network While Working From Home

How A Data Scientist Can Effectively Network While Working From Home

Rohit Chatterjee
networking
W3Schools

With COVID-19 impacting daily lives and disrupting industries around the world, a major setback is being witnessed by data scientists in terms of the networking they indulge in, to help the data science community move forward. With every major conference and meetups being postponed or cancelled at the moment, the chances of networking with new people coming into the community and discussing different topics have come to a stop. However, networking is a crucial aspect for any data scientist as it is imperative to remain highly active in the community. Due to the above-mentioned reason, data scientists are looking for ways to effectively network from home.

In this article, we shall discuss a few mediums through which a data scientist can network with the outside world without stepping out.

Blogging

Blogging is one very famous way of connecting among popular practitioners, but they can also be used by someone who is new in the industry. Most of the bloggers post new codes or tutorials to help the community with their new findings and tips. Blogging allows data scientists to showcase their works, which are open for feedback or queries. It also helps a data scientist to engage with the readers without any barrier and discuss any topic or share a thought process. 



Some of the top blogging platforms one can use are AIM Expert Network, Medium and more. One can also post their blogs on LinkedIn, which will gain them a good number of traction and attention. As a professional platform, LinkedIn is one of the best places to create connections.

Twitter

With more than 145 million daily active users, Twitter is an ideal place to connect with the data science community. From commenting on new data science papers to the latest developments in the data science space, Twitter serves as a powerful tool for networking. One can also share their thoughts on certain topics, raise questions and even share a quick glimpse of their latest projects. 

With the use of the right hashtags, a data scientist can reach a million of souls from the data science community. It is advisable to post or retweet real and interesting content on a daily basis with relevant hashtags. Not to mention, a data scientist can follow some famous data scientists such as Yann LeCun, Sebastian Thrun, Andrew Ng and more.

Quora & Reddit

Quora and Reddit are platforms to ask questions to people who can provide valuable insights. For those who are new in the community, Quora and Reddit are two good options to ask any question in regard to their projects or other topics related to data science since a number of experts are available on these platforms to help others through. If someone is already an established data scientist, they can reverse the role and reach others in the community who are looking for an answer or help with some other data science content. It is essential to have a complete profile with credentials mentioned in details for others to gain trust. 

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GitHub and Kaggle 

Whether one is an established data scientist or an amateur, GitHub and Kaggle need no introduction. Kaggle gives an opportunity to showcase one’s skill sets which can be made public for other’s feedback or comments. Comments, although a generic feature but helps to have a one-on-one discussion with other personnel over a particular topic. One can also work over old datasets from previous competitions and can come up with a new solution that can be listed over the public and private leaderboard. Moving on to GitHub, one can watch a particular project’s repository to stay up-to-date and can give feedback or ask several questions related to the project. It is advisable to follow some really cool people such as Ben Balter or Pifafu. Being on these platforms automatically helps one network with millions of people from the world of data science.

Some other ways

LinkedIn is one of the ways to reach the data science community by sharing blogs, certifications or achievements, which can draw a lot of attention. Not to mention, a number of people from the data science community are also on LinkedIn so connecting with them is an easy job to do. Moving forward, podcasts in recent times have garnered tremendous interest, so it is advisable to host a podcast. One can interview famous data scientists and discuss with them about the latest developments or can even feature in one as an interviewee. Some of the famous podcast that can be listed here are AIM podcast, Data Stories and The O’Reilly Data Show. 

As we bring this article to an end, we at AIM would like everyone to stay indoors and follow the guidelines laid down by government entities to remain safe from COVID-19.

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