Free STEM Resources for Girls and Women Who Want to Work in Data Science, Machine Learning and AI

According to the U.S. Census Bureau, women make up half of the nation’s workforce and more than half of its population, yet they are still significantly underrepresented across all areas of the tech sector, accounting for only 27 percent of STEM roles. Additionally, Black/African American women account for less than 3 percent of that workforce. These numbers drop even further when observing the number of women in niche tech fields like data science, machine learning and artificial intelligence (AI).

Up until 1985, women made up 50 percent of all computer programmers, according to FastCompany. Today, women in computer fields like AI make up just 7.4 percent of the overall talent pool. Similar numbers are reported for women in data science and machine learning — so what is causing this rapid decline?

We will explore several factors that keep women from entering these fields. We will also provide girls and women with the resources to help empower them to pursue careers in these underrepresented tech fields at different ages and life stages.

Why women aren’t pursuing STEM careers in data science, machine learning and AI

In order to encourage women to pursue tech-related career paths, it is important to look at why they currently don’t venture into these fields. There are many factors that may contribute, but it’s important to start from the beginning.

In the K–12 age group, approximately 74 percent of girls express a desire to pursue a career in STEM, according to an article by TowardsDataScience. During this time, girls and boys take math and science courses in roughly equal numbers, yet the interest for young women starts to decline around the age of 15–16. By graduation, there are even fewer girls looking to study in these fields, with just 0.3 percent of high school girls choosing computer science as a college major.

Not only do fewer women pursue STEM education, but an even smaller proportion of female STEM graduates go on to work in the sector compared to their male peers. What is the reason behind this gender disparity? Here are several factors that may explain the imbalance.

  1. Unconscious bias: Most people assume STEM fields to be masculine and humanities and arts fields to be more female-dominated, according to research examined by the American Association of University Women. The stereotype that STEM is for men is an obstacle to women’s success in the field. By encouraging women to identify and challenge this bias, STEM can move closer to gender equality. If you’d like to understand your own potential biases so that you can begin challenging them, here is a list of Implicit Bias Tests provided by Harvard, including one related to gender, science and career.

2. Media portrayals: According to a report by The Lyda Hill Foundation, in partnership with The Geena Davis Institute on Gender in Media, nearly 63 percent of STEM characters portrayed in the media have been men, and this figure hasn’t changed in the last decade. Additionally, the vast majority of STEM characters in entertainment media are white, with only 2 percent of STEM leads being portrayed by Black women.

3. Confidence gap: According to the National Council of Teachers of Mathematics, many girls lose confidence in math by third grade. Boys, however, are more likely to say they have strong math skills by second grade. This confidence gap may have profound effects on girls as they enter adulthood. A research study found that women are 1.5 times more likely to leave their STEM studies after their first college course in calculus, a crucial stepping stone for those pursuing a career in fields like data science, machine learning and AI. If you’d like to improve your own math skills, The Association for Women in Mathematics offers resources for women at all levels of mathematics. You can also explore our free math resources below.

How to encourage girls to pursue careers in data science, machine learning and AI

The fields of machine learning, data science and AI are always evolving, and these skills have an increasing impact in areas that many girls and women may have never considered. Introducing them to the broad range of relevant applications of these skills may help change this perception. Take these three female-led real-world use cases of machine learning, data science and AI as strong examples of what can happen when women are encouraged to explore tech.

  1. DigitalUndivided: Founded by serial entrepreneur Kathryn Finney, this nonprofit social startup uses data to solve the tech industries diversity gap. Specifically, the company uses data to launch development initiatives that create pathways into the innovation ecosystem for Black and Latinx women founders. To date, DigitalUndivided has helped more than 2,000 women grow their startups and raise over $25 million in investments for their businesses.

2. Hello Alice: Founders Carolyn Rodz and Elizabeth Gore realized they could make a positive impact on underrepresented business owners through technology. This idea gave rise to Hello Alice, the first machine learning technology to help business owners find their path by matching them to personalized opportunities and resources. Since its launch, the company has connected thousands of women nationwide and created an online community with more than 2,000 engaged members, including Black business owners, who make up 24 percent of the platform’s overall community.

3. COUTURME: This San Mateo-based startup is a personal designer for formal dresses, wedding gowns and bridesmaids styles. Founded by Yuliya Raquel, this platform uses AI to generate custom designs based on individual preferences. Prior to COUTURME, Raquel co-founded Tailornova and BootstrapFashion, two similar lines of tech fashion, generating $43 million in overall sales.

These are only a few examples of the possibilities that these fields can offer. If you’d like to read more inspiring stories, here is a list of 20 more women doing fascinating work in data science, machine learning and AI.

Free resources for girls and young women exploring STEM

Women are a vital part of STEM, and their perspectives are crucial to an inclusive landscape and workforce. Whether you’re an aspiring young data scientist, machine learning engineer or a seasoned professional, it’s never too early to start exploring the world of science, math and tech. Here is a list of free resources to help you on your journey from wherever your starting point is.

Activities for children

  1. Machine Learning for Kids — This is a free tool that trains a learning algorithm to recognize text, words, images or numbers/sets.
  2. Evolution Simulator — The browser-based evolution simulator allows users to practice game-based AI learning.
  3. Thing Translator — This game allows children to test the limits of computer vision while learning a new language.
  4. Minecraft Educational Edition — Children can now learn the basics of coding and AI through Minecraft.
  5. Kids Do Ecology — This game can help children learn about biomes, blue whales and data collecting. They can even create their own classroom experiment.

STEM resources designed for girls

  1. For Girls in Science — Sponsored by L’Oréal, this site offers a variety of STEM options, including a video blog, profiles of women in science, a list of summer camps and career info.
  2. Girl Scouts STEM Program — To support STEM experiences, the Girl Scouts have developed three leadership journeys and a number of STEM proficiency badges.
  3. PBS SciGirls — These short videos follow a group of middle school girls who are designing and building STEM projects.
  4. Women@NASA — This NASA resource includes video interviews and biographies of female NASA employees, as well as info on careers, events and outreach programs.
  5. EngineerGirl — Designed for middle school girls, this site offers interviews, quizzes, fun facts profiles, as well as engineering contests, clubs, programs and scholarships.

Free K-12 math resources

As mentioned above, the confidence gap for girls in math often begins early. To help combat this, here is a list of free math activities designed for various levels of young learners.

  1. Girls’ Adventures in Mathematics, Engineering and Science (G.A.M.E.S.) — Designed by University of Illinois, girls work on challenging camp projects and meet mentors.
  2. CoolMath4Kids — This is a great resource for kids who love playing games. It combines education with gaming to deliver extra mathematical fun.
  3. FunBrain — FunBrain is sorted by grade  for kids in Pre-K through 8th grade. It has every mathematical resource your kids need.
  4. MathBoard App — Designed to be played with parents, this app walks kids through the steps to solving addition, subtraction, multiplication and division equations.
  5. Perennial Math Tournaments — A virtual math tournament (via videoconferencing) for both teams and individuals. Open to children in grades three through eight.
  6. HOODA MATH — This is a website for math games divided by subject. Students K–12 and math lovers of any age can learn about numbers while having fun.
  7. Helping Your Child Learn Mathematics — Curated by the U.S. Department of Education, this website contains math activities for preschoolers and elementary students.
  8. NASA Kids’ Club — At NASA Kids’ Club: Players can practice their science and math skills to explore Mars, construct a fleet of rockets or search for NASA spinoffs.

Activities for teens

  1. Emoji Scavenger Hunt — This game uses AI to identify emojis in the real world using the mobile device’s camera.
  2. Quick Draw — In this Google-created game, players are asked to draw something and the AI has to guess what it is.
  3. Raspberry Pi Kit — This affordable credit-card sized computer can serve as a foundation for countless child- and teen-friendly machine learning projects.
  4. Tello — Tello by Ryze Tech is an impressive and programmable machine drone that is perfect for older children and teens that are interested in coding.
  5. Interplanetary 3D Sun App — Sponsored by NASA, this tool uses data from a fleet of NASA spacecraft. Watch solar flares, coronal mass ejections and more.

Free resources for young professionals and women in STEM

Data science, machine learning and AI activities for young professionals

At the beginning of a professional tech career in machine learning, data science or AI, it’s important to explore the different areas and roles within these spaces. The resources below provide the basis for foundational learning, networking, securing scholarships and fostering mentorships.

  1. 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning — This article by KDnuggets shares a list of 18 female thought leaders to inspire other young women.
  2. Women in Machine Learning — This annual technical workshop is designed for women to present their research in machine learning.
  3. Women in Data Science Scholarship — QuantHub currently offers a $1,000 scholarship award to support women pursuing an education in the data science field.
  4. Alice T. Schafer Mathematics Prize — This $1,000 scholarship is awarded to an undergraduate woman for their excellence in the world of mathematics.
  5. Aysen Tunca Memorial Scholarship — This is a merit-based scholarship for female undergraduate students seeking STEM field majors. The amount is $2,000.
  6. Society of Women Engineers — Offers nearly $1 million in various scholarships every year (totaling 260) to individuals who identify as female/woman.
  7. Center for Women in Technology — There are various scholarships for women in STEM, offered by major. Amounts and qualifications vary.
  8. Million Women Mentors — MWM is an organization that aims to spark the interests of girls and women in STEM by encouragement through mentorship.

Resources for women already in tech

Women already working in tech fields are able to ask deeper questions and reflect on their own knowledge gaps. These resources are designed to help them with continued learning, skills training and networking.

  1. Women in AI — This resource allows women in AI to actively collaborate via education, research, events and blogging.
  2. Women in Machine Learning — A technical workshop is designed for women to present their research in machine learning.
  3. Women in ML & Data Science — Dedicated to women and gender minorities who are practicing, learning or interested in machine learning and data science.
  4. Women Leading in AI — This global think tank for women in AI aims to address the bias that can occur within algorithms due to a lack of diversity and inclusivity.
  5. 3 Mantras for Women in Data — This article by MIT Sloan School of Management shares three mantras for every woman in data.
  6. American Association of University Women — AAUW is a nonprofit that promotes equity for women through advocacy, education and research.

Top machine learning, data science and AI podcasts for women

Podcasts are an efficient and effective way to stay current in the field of machine learning, data science and AI. These podcasts can allow women to learn from the top thought leaders and developers in the field.

  1. Women in AI — This bi-weekly program on Apple Podcasts meets with leading female minds in AI, deep learning and machine learning.
  2. Women in Data Science Podcast — Women in data science share their work, advice and lessons learned along the way with Professor Margot Gerritsen from Stanford University.
  3. Podcasts for Women Who Code — Women can listen and learn from other female thought leaders in tech with these podcasts on tech, work-life balance and more.
  4. Talking Machines — Hosts Katherine Gorman and Neil Lawrence interview experts and answer listener questions in this machine learning podcast.
  5. Data Skeptic — This weekly podcast featuring host Kyle Polich explores high-level data science concepts and research.
  6. Digital Analytics Power Hour — This podcast features a different data science topic every episode in revealing, conversational interviews with thought leaders in the industry.
  7. Founded: A Podcast Series for Women Who Lead — This Google for Startups podcast invites female tech professionals, including data leaders.
  8. Linear Digressions — Hosts Katie and Ben discuss a different facet of machine learning each week on this podcast.

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