Resources For Women In Data Science and Machine Learning

A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.


These are the stages of the current conversation on diversity:

  1. What is diversity?
  2. Why is it important? (demographic representation & good for business)
  3. Where to find candidates and make my organization diverse?
  4. How to retain employees from underrepresented groups?

The focus of this post is to share a comprehensive list of resources for women and non-binary people in data science for the goal of increasing diversity in the workplace.


There has been extensive research which shows that women are:

Some positive news is that research has shown that networking events for women do indeed move the needle on equality.

This guide was created to provide a resource for women and non-binary individuals in data science to accomplish the following in a friendly and supportive environment:

  • building community
  • networking
  • developing skills, education
  • sharing knowledge
  • finding jobs
  • advancing their careers
  • advocating for themselves and others

Male Allies

This resource will also be valuable to male allies who seek to diversify their organization, by proactively:

  • supporting and amplifying women
  • finding women speakers for conferences
  • recruiting women candidates for jobs

Guidelines for Engagement

Prior to joining any of the organizations or attending their events, it is essential to become familiar with these details:

  • Mission: read the mission statement of the organization
  • Code of Conduct: read the CoC of the organization
  • Membership: some of the organizations are open solely to women and non-binary people. Others are open to male allies. Be informed and respectful of their membership requirements before joining.

Analytics Conferences for Women

Women in Data Science (WiDS)

  • hosted at Stanford University
  • livestreamed around the world, 100K+ participants from 50+ countries
  • Annual conference in March
  • twitter hashtag: #WiDS2019

Women in Analytics

  • 2018 conference was March 15 in Columbus, Ohio
  • twitter hashtag: #WIA2018

Women in Robotics IV

  • 2018 conference is June 29 in Pittsburgh, PA

Women in Statistics & Data Science Conference

  • organized by American Statistical Association
  • 2018 conference is October 18-20 in Cincinatti, Ohio

Women in Machine Learning Workshop (WiML)

  • 2018 conference is December 3-4 in Montreal, Canada
  • co-located with NIPS 2018
  • other events co-located with COLT, ICML and more

Women in Tech Conferences

Women Who Code CONNECT

  • 2018 conference was April 28 in San Francisco, CA
  • for women in tech and software engineering


  • 2018 conference is August 1-4 in New York, NY

Lesbians Who Tech + Allies

  • 2018 conference is September 12-14 in New York, NY

Grace Hopper Celebration

  • world’s largest gathering of women technologists, 18K+ attendees
  • 2018 conference is September 26-18 in Houston, TX
  • twitter hashtag for 2017: #GHC17, #GHC2017


Below are various groups in the data and tech space. You can participate by:

  • subscribing to their newsletters
  • joining discussions: Slack team, LinkedIn or Facebook group, Twitter
  • joining a local chapter or meetup group
  • attending their talks and workshops (where available: hackathons, conferences)
  • being an event speaker; giving a workshop
  • volunteering your time

If there is not a local chapter, consider starting one and enlist the aid of others.

Tech Groups

Group Name Twitter
Anita Borg Institute @CommunityAnitaB
Black Girls Code @blackgirlscode
Black in AI @black_in_ai
Black Women in Computing @BWiComputing
Codebar @codebar
Django Girls @djangogirls
EdTechWomen @edtechwomen
Girl Develop It @girldevelopit
Girl Geek Dinner @ggdworldwide
Ladies Get Paid @ladiesgetpaid
Lesbians Who Tech + Allies @lesbiantech
National Center for Women & Information Technology @NCWIT
PyLadies @pyladies
PyLadies Remote @PyLadiesRemote
Rladies Global @RLadiesGlobal
Widening Natural Language Processing @WiNLPWorkshop
Women in Big Data @DataWomen
Women in Computer Vision Workshop @WiCVworkshop
Women in Machine Learning & Data Science @wimlds
Women in Tech Summit @WomenTechSummit
Women Who Code @WomenWhoCode
Write/Speak/Code @WriteSpeakCode

Diversity & Inclusion Groups

Group Name Twitter
Project Include @projectinclude
Power to Fly @powertofly
Women in Tech Fund @womenintechfund
Women 2.0 @women2

Regional Groups

Group Name Twitter
AI Club for Gender Minorities (London) @AIClubGenderMin
Ladies Learning Code (Canada) @learningcode

Groups with Membership by Application

Group Name Twitter
Tech Women @techwomen

Groups with a Membership Fee

Group Name Twitter
Apres @apresnyc
Tech Ladies @HireTechLadies
Women in Technology International @witi


Diversity Tickets

A Travis Foundation app which helps conference organisers reach out to minority groups by offering them ticket and travel grants

Geek Feminism

Geek Feminism provides various resources, including a template for Code of Conduct

Girls in Tech

GIT aims to accelerate the growth of innovative women entering into the high-tech industry and building startups through the creation of proprietary, innovative programming and strategic global partnerships.

Global Fund for Women

A global champion for the human rights of women and girls. They use our powerful networks to find, fund, and amplify the courageous work of women who are building social movements and challenging the status quo.

Published Lists for Finding Speakers

Here are curated lists of women in the data sciece and AI space. This is an excellent reference next time there is a Call for Proposals (CFP) for conferences.

Women in Open Source

Women are overwhelmingly underrepresented in open source. Here is a twitter list of Women in Open Source which was inspired by this twitter discussion.

Examples of Amplifying Women in Data

Here are some examples of how to bring visibility to women and non-binary people in the data space:

In honor of Women’s History Month, we are celebrating several ASA women who work in statistics and data science. These accomplished women were chosen because they inspired and influenced other women in their field. Read their biographies to learn why they chose statistics, who influenced them, and what all they have accomplished.

Slack Teams

  • PyLadies
  • R-Ladies: send an email to to request your invite
  • WiMLDS: email for invite link (for women and non-binary)

For Pre-college Students

Roadblocks to gender parity are present early in education. Here are some groups who are impacting the participation and retention of young women prior to college.


A global network of free, volunteer-led, community-based programming clubs for young people. Anyone aged seven to seventeen can visit a Dojo where they can learn to code, build a website, create an app or a game, and explore technology in an informal, creative, and social environment.

Girls Who Code

Girls Who Code was founded by with the mission to close the gender gap in technology.

AI 4 All

The core model is educating and supporting the next generation of diverse leaders in AI through summer camps for underrepresented high school students at leading universities including Stanford, UC Berkeley, Princeton, Carnegie Mellon, and Boston University.

Supporting Organizations

You can support any of these organizations by:

  • Subscribing to their newsletters
  • Connecting on social media
  • Becoming familiar with their mission and projects
  • Sharing their mission and projects with others
  • Building a relationship with the organizations
  • Participating in events and networking
  • Volunteering your time
  • Sharing job postings and internship opportunities
  • Donating to their cause
  • Becoming a corporate sponsor

Data Science Resources

This resource list includes podcasts, conference list, newsletters, learning materials and more.


Bio: Reshama Shaikh is a freelance data scientist/statistician and MBA with skills in Python, R and SAS. She worked for over 10 years as a biostatistician in the pharmaceutical industry. She is also an organizer of the meetups group NYC Women in Machine Learning & Data Science and PyLadies. She earned MS in statistics from Rutgers and her MBA from NYU Stern School of Business.

Original. Reposted with permission.


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