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R-Ladies Baltimore (Bmore) is excited to host a meetup on Tuesday February 22, 2022 from 3-4:30pm (virtual meetup on Zoom). We will have an informative presentation from Claire McKay Bowen (@ClaireMKBowen, https://clairemckaybowen.com), Principal Research Associate at the Urban Institute. She will be giving an introduction to data privacy preserving applications in R. Materials for the presentation can be found at https://github.com/ClaireMcKayBowen/r-ladies-baltimore-privacy-talk

WHO is this for?

  • If you are new to R and want to learn how to use data privacy preserving applications
  • If you know R, but want to expand your knowledge about maintaining data privacy and confidentiality in your work

If any/all of this is true, this event might be for you!

AGENDA
3:00–3:15pm Who we are | Announcements | What's next
3:15–4 pm Presentation: Data Privacy Preserving Applications in R
4–4:30pm Networking -- Any more Qs?

Details on the location:
The Zoom information will be sent to those who RSVP the week before the event.

FAQ:
Q: Are men welcome to attend R-Ladies events?
A: Yes, men are welcome at this event. Our mission is to encourage more women and non-binary individuals to get involved in the R community. However, we ask that cis men come as guests of women and gender minorities to our events to promote a sense support and encouragement for women and gender minorities in the R community. Please help us by spreading the word about R-Ladies. Please also take the time to review our code of conduct before you attend: https://github.com/rladies/starter-kit/wiki/Code-of-Conduct

The term "women and gender minorities" is inclusive of individuals who identify as female (trans and cis), trans-men, non-binary, genderqueer, genderfluid, agender, intersex, and all minority genders).

Important to know:

Finally, for more information:
email: bmore@rladies.org
twitter: @RLadiesBmore
facebook: https://www.facebook.com/RLadiesBmore/
meetup: https://www.meetup.com/rladies-baltimore/

Big Data
New Technology
Data Management
Data Privacy
Open Source

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