Connecting With The Data Community: Where to Start

One of the most effective ways to advance your skills and your career in the data industry is to get connected with like-minded people. Finding your community within an industry space can seem daunting at first, but there is value in establishing a network of people you can share your experiences with. In this brief blog, we will first talk about why you may want to start to connect with the data community. Later, we will touch on how this actually done, and the few small steps you could take to quickly expand your data network.

Why should I become a part of the data community?

Data analytics is a rapidly expanding industry space that is creating new careers and opportunities for people every day. Something that I love about this space is that data analytics is not insular, but rather it finds itself within a ton of other industries. There are niche groups for data analytics within tech, healthcare, insurance, manufacturing, and many more! More and more industries are looking to implement data analytics within their organizations which means more and more opportunities to connect with people that are taking similar paths as yourself.

This rapid expansion of the space has resulted in an ever-increasing demand for people with skills in data analytics, so it is more important than ever to get your name and face out there in the community so companies and recruiters can get to know you and the content you create. Up to 80% of jobs are filled through networking, and those numbers tend to increase in more niche, technical industries. This is one reason why there is value in finding a community to connect with.

With so many jobs being filled through networking, finding a place within an industry community is more valuable than ever!

The second reason why you will want to find a data community is that you'll have a place where you can receive tons of support as you learn new tools and improve existing skills. Every day people are sharing their data products and soliciting feedback from an ever-growing community of data analysts. At the same time, people are creating youtube videos, blogs, and lessons that are helping new learners gain valuable skills. One of the main complaints I have heard from new learners is that they feel isolated during the learning process and therefore begin to feel alienated from their work. When you are sharing your experience and have people giving you consistent feedback throughout the process you will become more motivated to advance your skills.

I want to find my data community! Where do I start?

Working at The Data School New York, I have been fortunate enough to become a part of the data community in a variety of different ways. One of the first ways I started to talk to people about data was through LinkedIn. This well-known networking website has a bunch of 'data analyst influencers' who post about new job opportunities, personal projects, and their experiences in the industry space. One of the first things I did was join the Business Analyst Professional group and chat with people about their job experiences. Since then I have connected with data analysts all around the world! We share our dashboards, provide feedback to each other and talk about the struggles of the learning process. There are also a few well-known analysts that I have followed for a while. They tend to talk about the job market for analysts and frequently share entry-level opportunities for up-and-coming data analysts. I will be linking some great people to connect with / follow on LinkedIn at the bottom of this blog!

This is one of the many groups you can join on LinkedIn for connecting with like-minded data analysts.

Believe it or not, Twitter is also a great place to connect with data analysts! People love to post their dashboards for feedback and share job experiences with the #DataFam community. Although it is less network focused than LinkedIn, Twitter is a great place for seeing some amazing visuals. I will put some common hashtags used on both LinkedIn and Twitter at the end of the blog.  People also post a bunch of data challenges on Twitter, which is another great way to connect with the community...

Data challenges such as Workout Wednesday and Makeover Monday are weekly opportunities to improve your data visualization skills while connecting with the community over a common project. For Workout Wednesday, you are tasked with recreating a visual and sharing your process with other data analysts on your social media of choice. Makeover Monday challenges you to re-visualize a dashboard in a new, unique way. Similarly, this is to be shared within the community for feedback.

An example of a Workout Wednesday challenge. Try one out yourself!

Finally, there are both In-Person and Virtual Events that you can attend to improve your skills in Tableau (or whatever BI tool you are using) while forming new relationships with data analysts. There are a bunch of Tableau User Groups that have popped up in the last few years in different states and I am constantly seeing new data-related events being advertised on LinkedIn. It was the virtual data school meet and greets I attended a few months ago that connected me with people that have changed my life. I highly encourage using these events to establish relationships with companies that you can call upon later.

Don't be afraid to connect with people! It will feel weird at first but this is how you expand your presence within the community. Everyone will have a unique experience; share yours with the world!

Some quality LinkedIn data people (they can likely also be found on Twitter):

https://www.linkedin.com/in/andykriebel/

https://www.linkedin.com/in/dustinschimek/

https://www.linkedin.com/in/alex-freberg/

https://www.linkedin.com/in/alisha-dhillon-146552142/

Common data-related hashtags you can find on both Twitter and LinkedIn:

#data #dataanalytics #datafam #dataviz #datavisualization

Make sure to connect with me on the various platforms and reach out if you ever have any questions. Good luck!

Author:
Tristan Kelly
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