Authors: Lara Karki, Dana Priest, Gabe Dubose, Zajerria Godfrey, Annabel Rothschild, Ben Rydal Shapiro, Betsy DiSalvo
Quick links:
- Our publicly available curriculum
- SIGCSE TS 2025 page for our paper
- Pre-print PDF (coming soon)
- Notebook LM-generated podcast about the paper
How can we design computing education to include adults without formal technical backgrounds? This question is central to DataWorks, a program at Georgia Tech that blends work with semi-formal technical training and career development. Unlike traditional coding bootcamps or degree programs, DataWorks provides full-time, paid positions for adults from a diversity of backgrounds to develop data skills through real-world projects.
This post summarizes insights gained over four years of DataWorks, focusing on how workplace learning can support novice adults in entering technical fields and what lessons such programs offer for others exploring similar models.
DataWorks: What It Is and How It Works
Founded in 2020, DataWorks employs adults local to Atlanta, “Data Fellows,” for one year to work on client data projects while undergoing training in Microsoft Excel, critical data literacy, Python, and career development. These projects come from nonprofits, researchers, and businesses, offering Fellows real-world tasks that reinforce their learning.
The curriculum is designed to integrate into workplace practices, encouraging Fellows to connect technical training with the needs of their projects. Weekly “skill-share” sessions further solidify learning, as Fellows demonstrate concepts they’ve applied in their work.
The goal is not simply to teach technical skills but to provide a meaningful and accessible way for participants to transition into technical roles and develop their careers. We focus on pathways to jobs not typically considered computing roles, but which primarily involve computing work, for example, administrative work.
Reflections: Challenges and Evolution
Over the years, DataWorks has evolved through a process of trial, reflection, and adaptation. Here are some reflections on the program’s challenges and how it has developed:
- Balancing Structure with Flexibility
Early cohorts preferred on-demand, project-based learning rather than structured training. While this approach aligned with immediate work needs, it lacked the consistency necessary for sustained learning. Gradually, the program adopted a more structured curriculum while retaining the flexibility to adapt to the unique needs of each cohort. - Shaping Roles and Responsibilities
DataWorks promoted former Fellows into leadership positions, such as Project Manager and Training Coordinator. These shifts brought stability to the team and ensured that training aligned with workplace realities. This approach also empowered Fellows to take ownership of the program’s growth. - The Importance of Real-World Context
Authentic projects, like processing large datasets for civic organizations, demonstrated the interplay between work and learning. Fellows learned to apply Excel and Python training to automate tasks, reinforcing technical concepts and fostering a problem-solving mindset. - Navigating Systemic Barriers
Despite the skills gained at DataWorks, Fellows faced challenges when transitioning to data-focused jobs due to barriers like degree requirements or a lack of professional networks. These obstacles pushed the program to broaden its focus beyond traditional analytics roles, exploring pathways like grant administration and freelance work. - Being a “Novel” Program
DataWorks often defies easy categorization. It is neither a formal training program nor a traditional job. This uniqueness is both a strength and a challenge, requiring careful communication with potential Fellows, employers, and partners.
Takeaways: Insights for Similar Programs
From these experiences, DataWorks has distilled several insights that may be valuable for others considering similar workplace learning models:
- Adults’ Motivation is Complex
Participants bring diverse goals, including financial stability, career transitions, or finding alternatives to higher education. Programs must meet learners where they are, offering flexible pathways that align with their circumstances. - Adaptability Is Essential
Flexibility in responding to challenges—whether restructuring roles, revising training, or broadening career options—has been key to sustaining the program. - Work Informs Learning, and Learning Informs Work
The interplay between client projects and training reinforces learning and ensures that skills are immediately applicable. This symbiosis benefits both Fellows and the organizations they serve. - Broadening Career Pathways is Crucial
Technical training alone may not open doors if systemic barriers remain. Programs need to prepare participants for a range of roles, including nontraditional and entrepreneurial pathways. - Workplace Needs Challenge Traditional Models
DataWorks’ focus on workplace-relevant skills shows how computing education can move beyond the traditional classroom or bootcamp model. Programs should emphasize practical applications and recognize that success may look different for each participant.
Looking Ahead
While DataWorks has made significant strides, it continues to face challenges like scalability and the systemic inequities that persist in hiring practices. Its model, however, offers a promising blueprint for integrating work and learning in ways that support adult learners from diverse backgrounds.
Workplace learning programs like DataWorks provide a valuable alternative for those excluded by traditional education systems. By reflecting on and sharing experiences, we can continue to refine these approaches and broaden access to computing education for all.