How Metis is Promoting Diversity in Data Science
If you ask Metis Senior Data Scientist Sophie Searcy, one of the most rewarding parts of working as a data science instructor is having the chance to shape the next generation of data scientists.
Of course, that opportunity comes with responsibilities,including ensuring members of underrepresented groups have access to rewarding careers and that all Metis graduates leave the program with an understanding of the ethical implications of the effect of data science on society at large.
Searcy thinks about, writes about, and works within this space a lot. Most recently, she wrote an article for InformationWeek titled Why Data Scientists Should Make a Commitment to Diversity. In it, she writes:
"When it comes to data scientists, we've held the title of 'best job in America' for three years in a row, so we have a special obligation to lead the way. An ideal aim would be a population of data scientists that is roughly proportionate in gender, ethnicity, and other demographic measures to the broader U.S. population."
In pursuit of these goals, Metis has taken the following steps over the years:
1. Incorporating Ethics More Directly into the Bootcamp Curriculum
A data science training company has a responsibility to train students in the ethical use of their skills. Knowing that the field is constantly changing, our approach is rooted in teaching what we call 'practical' ethics within our curriculum.
"Instead of teaching rules, we teach students frameworks for evaluating the ethical implications of a project or business decision," said Searcy in a recent interview. "We teach these frameworks, which are far from black-and-white, and ask students to apply them to several case studies in a very discussion-focused lesson. The ethics content is one of my favorites to teach because it's where I see students thinking most critically and collaboratively."
In that same InformationWeek article, Searcy used the example of algorithmic violence as a byproduct of a failure to consider ethics in technology.
"The term, coined by Mimi Onuoha, refers to the ways that algorithms or automated decision-making systems inflict harm by preventing people from meeting their needs," she writes. "Civil and mechanical engineers commonly take courses on the ethical challenges involved in designing physical things, so why shouldn't data scientists learn to think critically about how our work can lead to harmful consequences, too?"
Going a step further, we believe that teaching ethics can't be contained only to lessons labeled "ethics." We have a project-based bootcamp curriculum, so in a classroom of 25 students covering 5 projects each, students are working with instructors to plan, scope, and execute 125 total projects over 12 weeks.
"It's in these moments that instructors are working with students to make sure that the implications of their projects are really understood," said Searcy. "They can ask questions like: Is it appropriate to use zip code as a feature in a model for approving loan applications? Or, this model works on a toy dataset but how would you collect this data from people in a real business application?"
While we can't tell our students what they should decide about the specific ethical decisions they'll be confronted with once they enter the data science field, we can make sure that all of our graduates leave with an understanding that their jobs have ethical implications and have the necessary skills to evaluate those implications.
"My goal when writing our ethics content and teaching it is to make sure that, if nothing else, all of my students understand the ethical implications of the things they do," said Searcy.
2. Offering Bootcamp Scholarships
We know that diverse points of view enrich all learning and professional experiences (and really, all experiences in general). Diverse voices improve our products and our services, and inform our perspectives.
Because of this, we offer $3,000 bootcamp scholarships to women, members of underrepresented demographic groups, members of the LGBTQ community, and veterans or members of the U.S. military.
We are committed to creating a culture of inclusion within this exciting and growing field, and we aim to foster an equal and representative data science community filled with individuals of all technical, educational, and personal backgrounds.
3. Providing Access via GI Bill Benefits + M-1 Visas
In that same vein, we strive to make our bootcamp as accessible as possible for all. This includes offering the M-1 student visa to international students so they can attend the bootcamp at our New York City and San Francisco campuses.
Additionally, we're proud to offer GI Bill® benefits to student veterans at our NYC campus who want to develop their data science skills. We were approved by the New York State Approving Agency for the training of student veterans and other eligible persons, and we hope to obtain approval for the same funding at our other locations soon.
4. Building a Diverse Team
We practice what we preach by promoting diversity on our internal team, as well as in the classroom. We're building a diverse team across leadership roles, data science staff, as well as within our engineering, careers, program operations, marketing, and admissions departments. We're proud that more than 50% of our employees are women and we continue to prioritize diversity in the workplace.
"When we're building our team, this is something we think deeply about," said Searcy. "It's more than simply hiring a diverse team. We want people who understand that anyone, regardless of background, can succeed in data science and who put that belief to work."
We know we still have work to do – and we're ready to keep at it, because we believe in the importance of diversity in data science.
In her article, Searcy said it best:
"A fairer society, better products and services, and the prevention of harm to the most vulnerable among us: all of these and more can be brought about if we as an industry make a commitment to hiring and cultivating diverse talent in data science."