Building Your Data Science Network: Finding Community

Where to find other people in your field.

Emily Robinson


January 10, 2018

So you’ve heard you’re supposed to network. That’s the key in getting a job or establishing a reputation in your broader field, right? And it’s true that the importance of having a good network is supported by a lot of social sciences research. But if the thought of networking makes you cringe, you’re not alone. Many people equate networking to sending out millions of unsolicited Linkedin requests with no message, handing out 20 business cards at a meetup once a week, or sending emails to prominent data scientists with the subject line “Can I pick your brain?” That’s not very appealing to most people, and it turns out not very effective either.

Building a network is not a numbers game, and it’s not about looking to get the largest personal benefit from the least time invested. Fulfilling networking is, at its heart, about building great relationships and becoming part of a community. People (even us strong introverts!) are social, and we generally have more fun and are more productive when we can lean on each other for help. As I’ve shared before, part of the reason I choose to program in R is because I’ve found an incredibly supportive community:

So how do you become part of this community? I’ve written this two part post to try to share some strategies that have worked for me. This first part is focused on growing your community generally. If you’d like to learn about reaching out to specific people, check out the second part.

Attend Meetups

There are a lot of great data science meetups. If you’re in New York, I highly recommend the New York Open Statistical Programming meetup; if you’re a woman, check out groups like RLadies, Women who Code, and PyLadies, all of which have chapters all over the world. Stick around after the meetup; there might be an after-event (the NY Open Statistical Programming meetup invites everyone to a bar), but even if there isn’t, some people will likely stay around and you can meet them when it’s less crowded and you have a natural discussion topic (the talk you both just heard). You can also talk to the organizers; they’re almost definitely friendly folks and may be able to introduce you to others who share your interests.

If you’re nervous about going somewhere where you don’t know anyone, you’re not alone! I definitely have been. Steph Locke, who runs her own data science consultancy, has shared some great tips for tackling different parts of social anxiety. You can also bring a friend, but try not to just talk with them the entire time. And if you go alone, look for pairs of people or groups following the pacman rule:

If you’re looking for a conversation starter, remember that at a meetup or conference, you’ll generally have the first-order topic of “we are here because of R (or machine learning, python, etc).” But there are some meatier second-order questions you can dive into, like: Why this meetup? What got you interested in R? What are your favorite learning resources? (Thanks to my colleague Jake Voytko for these suggestions). People love to talk about themselves, and everyone is someone you can learn from. You don’t need to try to meet as many people as possible or make sure you talk to the most important or well-known person in the room; if you have one enjoyable conversation at the meetup with someone new, I would call that a success.

Try out Twitter

Getting started with Twitter can be intimidating. How do I untangle these nested threads of replies, when should I use a hashtag, and how do I even get any followers? When I first started, I just followed people and didn’t tweet anything myself. I felt a bit ashamed about that - shouldn’t I be adding value and interacting? But I now think that’s actually a great way to start out. You can just use Twitter as learning platform to get a picture of what people are talking about and find some interesting blog posts. My main advice to my past self would be to put a profile picture up - don’t be an egg!

There’s a great, friendly R community on there. If you’re looking for people to follow, some good people include Hadley Wickham (hadleywickham), Jesse Maegan (kierisi), David Robinson (drob), Jenny Bryan (jennybryan), Mara Averick (dataandme), Julia Silge (juliasilge) and Maëlle Salmon (ma_salmon).

Check out Rachel Thomas’ post for more good advice about getting started with Twitter. This can be a great way to connect with people if there aren’t many (or any!) meetups in your area. For example, Daniela Vazquez, now a data scientist at IDATHA, didn’t have a local RLadies group in her country Uruguay, but through Twitter found out about the start of a Buenos Aires chapter and became a co-organizer.

When you are ready to start tweeting, there’s a lot of things you can talk about. You can announce when you’re going to a conference, ask technical questions, and share your work. It can also be a way to break the ice when you finally meet someone in person. For example, when I ran into Jenny Bryan last year at RStudio::conf, I felt more comfortable introducing myself because I’d been following her on Twitter and felt I “knew” her a bit. You can even say a “virtual” hello (as written about by Rick West, along with some excellent insights on building a personal brand) to someone beforehand if they’ve said they’ll be speaking or attending a conference you’ll be at.1

On the technical question side, you can try out the rstats hashtag. Just the other week I tweeted out asking if anyone could advise on setting a global color palette for ggplot2:

I got a lot of good responses, including one introducing me to the awesome ggthemr package. I then paid it forward by tweeting about the discovery:

You’ll often even find Hadley Wickham answering questions on Twitter; I think it’s more polite to use the rstats hashtag than to @ him directly, but he usually answers questions specifically directed to him as well.

Finally, if you’re posting about your first blog post, you’ve got one prominent tweeter with you (with 17,000+ followers!) - @drob. If you point him to it, he will read and retweet your first few posts:

Combining the two

One of my favorite things to do is live tweet talks. It’s gotten to the point where it’s become expected:

I like it because it serves as notes for myself, it’s a way to share key points from the talk with people who couldn’t attend, and it gets the speaker some more visibility. If you’d like to try it, here are some tips from a live-tweeter master, @drob:

I’ll end this first part with a similar promise to Dave’s: if you live tweet a talk or share a great data science resource on Twitter, let me know with a tweet and I’ll retweet. Let’s continue building a friendly, supportive community together.

If you found this post useful, you might be interested in the book on data science careers I wrote with Jacqueline Nolis, “Build Your Career in Data Science,”, available for 40% off with the code buildbook40%.


  1. A public (i.e. not a private message), brief hello is friendly. A virtual “I see you’re going to be at the NY R conference, what hotel are you staying in, where are you eating, will you be wearing the same black dress as last year, because you looked really hot” is not. Harassment and stalking are serious and common problems. Keep messages professional; don’t be this person: ↩︎