Girls are using data analytics to figure out what’s fair – and what they are capable of

Mariama answers a question in a lesson about probability and fairness in a class of the Pre-G3 Elsevier Data Analytics Preparatory Program

Girls in under-served communities are learning data science in a program by Girls Inc. of NYC and the Elsevier Foundation

BRONX, NY — In a brightly lit classroom, middle school girls are chatting, tossing dice and penciling their scores on an index card. It may seem like fun and games in this playful after school program, but stick around and you will see that there’s much more to it.

What these students are about to learn may well impact them far into the future.

“This program helps you understand challenges that you’re going to meet in life and how to overcome them,” said 11-year-old Rodiyat. “And they don’t treat you as a child now because they know that you’re not going to be a child forever,” she added. “They teach you stuff that you’re going to come back to later on.”

The “stuff” she’s referring to includes everything from the fundamentals of data analytics to how to think, question and learn.

Rodiyat is enrolled in the Pre-G3 Elsevier Data Analytics Preparatory Program, developed by Girls Inc. of New York City in partnership with the Elsevier Foundation. Here, girls in 10 New York City public schools are immersed in the world of data analytics and how data can be used to interpret nearly every aspect of their lives. This class meets at the Young Women’s Leadership School of the Bronx.

Students are taught data science through the lens of social justice issues:

  • They review articles about current events and analyze the metadata to come to their own conclusions.
  • They explore ethical usage of data via databases used by the College Board, health insurance companies, social media, and health and wellbeing tracking devices like Fitbit and Sleep Cycle.
  • They use data to improve their community by exploring the work of local “game changers”— women who are making a difference in how data is being collected and analyzed. These include Joy Buolamwini, a Ghanaian-American computer scientist and digital activist who founded the Algorithmic Justice League, an organization that challenges bias in decision-making software, Giorgia Lupi, an information designer who takes a humanistic approach to data by designing visual narratives that reconnect numbers to what they stand for: stories, people and ideas.
  • To visualize their own data, they learn to use Excel and Tableau software.

For Rodiyat, this education is preparing her for an ambitious career goal. “I want to be a doctor,” she said, “and I know that in order to do that, you have to study a lot, and you have to make sure you read, and I think Girls Inc. is helping us do a lot of that.”

Rodiya with charts
Rodiyat, 11, poses in front of the charts she and her classmates made for an activity called “Is it a fair game?”

Letizia, 10, also has high ambitions:

People underestimate females way too much. I want to become a leader of other girls when I grow up to teach them that females can do what they want.


It stems from when she was one of just three girls to make the school soccer team back in Milan, Italy.

The boys were like, ‘Are you sure you can be here? You know you’re going to get kicked everywhere, and you know, like, you’re probably going to slip and you’re probably going to get hurt, and this and that – so don’t go whining back to us to say that we didn’t warn you.’

Of course, none of that happened. “I showed them,” she said, grinning.

Ciara with dice game
Ciara and Alina play a game with dice.

While this course is new, Girls Inc. of NYC has introduced hundreds of high school girls to data analytics through Generation Giga Girls (G3): The Moody’s Data Analytics Program. The demand for more programming serving even younger girls sparked the creation of this program, and the Elsevier Foundation was a natural partner because of Elsevier’s role in using information analytics to help researchers solve some of the world’s most pressing problems.

“From the beginning, we recognized the Pre-G3 program as a perfect fit for both the Elsevier Foundation and Elsevier,” said Ylann Schemm, Director of the Elsevier Foundation. “It touches on so many areas we care deeply about: gender, technology, capacity building and under recognized talent — areas we can really contribute to.”

Through the G3 program, 12-year-old Ciara has been getting a head start on technology studies by learning coding, so data science was a natural extension of her education. “When you’re doing coding, you have to keep track of the data,” she explained. Plus learning to analyze the data helps her see the big picture of what she does in coding.

Getting a holistic education

Stephanie, 13, plays a data analytics game with dice.

At 13, Stephanie is learning to overcome an issue many girls her age can relate to as they set high standards for themselves and compare themselves to their peers. “Sometimes I feel like I can’t do enough – like my grades,” she said. “I compare myself a lot to other people, so I feel like I’m not as good as them.”

Here, in addition to learning skills to improve her grades, she’s learning to practice growth mindset. This approach involves teaching students that intelligence is a skill they can develop with effort rather than a fixed trait they are born with – and research is showing that it can help girls with math and science.

Pre-G3 addresses key issues by training students in a developmentally appropriate manner by building tangible skills for looking at math. After completing the year-long course, girls receive math and science credits that will count towards middle or high school graduation.

In keeping with the Girls Inc. model, students also receive a holistic education that includes media literacy and critical thinking skills. The course builds a foundation for data literacy by teaching the hard skills necessary to prepare for the school’s advanced data analytics courses while addressing the broader questions of “What is data?” and “Why should we care about data?”

“By the end of the school year, we will expand the data literate population, capable of understanding, generating and using data,” said Andrea E. Oliver, STEM Program Manager for Girls Inc. of NYC and a doctoral candidate in Urban Science Education at the Graduate Center, City University of New York (CUNY).

A growing demand for data literacy

Girls Inc class
Marguerite Copeland, Program Specialist for Girls Inc. of NYC, flips a coin in a lesson about probability.

With the ever-increasing demand across professions for skills in managing data, high-level math and science courses are essential to prepare girls of color for college and careers in data science. However, black and Latino students “are being shortchanged in their access” to these courses, according to federal civil rights data.

This year, the US Department of Education reiterated the need for STEM education for all students:

In an ever-changing, increasingly complex world, it’s more important than ever that our nation’s youth are prepared to bring knowledge and skills to solve problems, make sense of information, and know how to gather and evaluate evidence to make decisions.

They emphasized that all children should have access to STEM education and that “a child’s zip code should not determine their STEM fluency.”

This is why the Elsevier Foundation and Girls Inc. of New York City partnership is so important. By increasing the number of girls enrolling in data analytics by improving their core skills, confidence, and resiliency, the Pre-G3 program is building the next generation of women equipped for success in filling the demanding jobs our economy is relying on.

“We try to give them a lot of different opportunities to get more girls into the field,” Oliver said.

Andrea E. Oliver, STEM Program Manager for Girls Inc. of NYC, works with Letizia. Oliver developed the activities for this program.

Probability game: “Is it a fair game?”

Andrea E. Oliver, an award-winning STEM educator who has taught science for more than two decades, designs the lessons for this class. On this day, students played a game with dice and then were asked to analyze the results to determine if the game was fair.

  • Group participants into pairs.
  • One participant in the pair will play for 0,1,2 while the other plays for 3,4,5.
  • Each participant pair receives a probability die (2 different colors).
  • Each participant gets to roll the die once, then take the two numbers and find the difference.
  • For instance, if one student rolls a 5 and his/her partner rolls a 3, they would take the larger number and subtract it from the smaller number. In this case 5 – 3 = 2. When they determine the difference, they will be able to see who receives a point based on which camp they belong to: 0,1,2 or 3,4,5.
  • They will play this game 25 times and tally their results on an index card.
  • Participants will discover that 0,1,2 often wins the games. But is this game fair?
  • Participants create a data table and analyze the data to demonstrate that the game is not fair. That’s because 0,1,2 has a total of 15 possible outcomes whereas 3,4,5 has a total of 6.

* Modified from the Difference Game and created by Andrea E. Oliver.