Growing your Data Science Community

Here are a few tips for finding the people to join your data science community:

• Use LinkedIn to see who in your area or at your company may also be using data science. For example, who is also part of the “Python Developers Community” or “The R Project for Statistical Computing” groups?
• Starting small is great. Find a core group of people to get the group going consistently.
• Create a Zoom/Slack/Teams/other channel for people to connect with others.
• Try scheduling a Lunch & Learn session. Tell people to invite their friends. They may not realize how powerful their own networks are. When you get people together, you can share what you’re all working on from different departments.
• Give people an understanding of what’s possible to help it spread organically.
• Partner with the business. Generally, business users are not programmers and through. discussions you can ask how they’re going about solving specific problems today and how you can tackle that differently together. Those relationships are so important.
• Lead small cohorts and focus on exploratory data analysis. You can create these cohorts with business analysts and pair them up with people who may be in more technical data analysis roles. If you can get them to learn together on a cadence, they form relationships, understand each other’s problems, and learn data science simultaneously.
• Introduce Shiny apps for business users to open up their eyes to the power of data science (or simply a code-first approach to business analysis). By automating their team’s work, you will likely see a lot more interest from others to learn.
• Build a blog to share events and past sessions with people interested in joining.
• Ask those who come to an event (or those who express interest) what they would like to learn and also what they can teach others.

For more tips, listen to this full conversation on building community in the Data Science Hangout with Tori Oblad, Enterprise Data & Analytics Officer at WaFd Bank.

There’s no standard format for an event, but this sample agenda and list of types of meetups will help you get started and maybe even spark some ideas of your own.

Javier Orraca, Senior Manager, Data Ops & Analytics at Bloomreach says, “In my prior role, we hosted a monthly R user group with the following agenda:

1. Intro (3-5 min): We provide an overview about the guest presenter we will host and their presentation topic (always an internal R user that is doing something neat with R that could benefit our R programming community)
2. Company Data Science Updates (5 min): We discuss data warehouse and system updates that might impact our internal community
3. Package Updates (5-10 min): Here we cover major updates to existing popular packages and we update the audience on new and improved internally developed packages
4. Guest Presentation (30-40 min)
5. Call for Ideas / Topics to discuss in future meetups (5 min)”

Yanina Bellini Saibene, R-Ladies Global Team, MetaDocencia Core Team & LatinR Chair has hosted the following types of meetups:

1. Training that takes several meetings. The most common are 4 weekly meetings. The introduction to R is the one that is most requested. For face-to-face meetings, universities, research institutes and municipalities will often provide locations with computers. For on-line, R-Ladies Global provides a Zoom account.
2. Meetup to complete scholarships to participate in conferences. For this one-day event, we got together and helped write or review scholarship applications to attend important conferences like useR! 
3. Metameetups. These events are organized between several chapters of the same country or that share the same language. They last between 1 to 3 hours. Talks or workshops are presented. This type of event was a huge success with the possibility of doing it online.
4. Joint meetup with a conference. We have hosted tutorials, conducted tutorials, or given talks about R and/or R-Ladies during particular conferences. There are also social events such as all R-Ladies eating together.
5. Meetup – Your first talk. An event where a safe and friendly space is generated for people to practice and deliver their first talk. The talk lasts between 5 to 10 minutes, after which feedback is provided.
6. Book club meeting. Weekly or monthly meetings are held where different books related to R and data science are read and discussed (and if they have exercises they are solved together). The pandemic made it possible to organize these events between several chapters because of the online aspect.

Encouraging others to get involved is integral to sustaining a community. Follow these tips to get others involved in planning, sharing, and even leading topics at your next event:

1. Spend 1:1 time with someone and say, “We’re planning to present this; would you be part of that? If you need any help prepping I would love that, it’d be great to debut your work because of x, y, z.”
2. Try not to restrict the group to a certain topic or tool. Perhaps say, “We just want to share something interesting with each other that might benefit someone else”
3. Offer a series of short, lightning talks so that someone might feel more comfortable presenting, especially if it’s their first time.
4. Create a Zoom channel/Slack workspace/similar to help each other and be able to see how other teams are problem-solving with analytics and data science.
5. Share something with bookdown to Posit Connect (or another similar tool) to easily share documentation someone would need to join the group, past sessions, and info for those interested in getting started learning.
6. Try asking, “What gap have you mastered lately?” This will get people talking from an “I learned standpoint” vs. an “I don’t know how” standpoint.

A special thank you to Edgar Gallo, Eric Nantz, Evan Munson, Frank Corrigan, Javier Orraca, Rick Scavetta, Tori Oblad, and Yanina Bellini Saibene for sharing these tips.