Monique Tepe is a data journalist and visualization designer known for rigorous methods, clear storytelling, and community focused education. Her work translates complex civic and social topics into accessible graphics.
This article outlines core aspects of Monique Tepe’s practice, highlighting her projects, visualization principles, teaching, and practical guidance for aspiring creators.
| Full Name | Focus Area | Primary Platform | Notable Collaborations | Public Contact |
|---|---|---|---|---|
| Monique Tepe | Data journalism, civic data visualization | GitHub, Medium, talks | News orgs, open data communities, education programs | Portfolio site, Twitter, workshops |
Approach to Data Storytelling
Clarity and Accessibility
Monique Tepe emphasizes charts that are legible on mobile devices and readable by diverse audiences. She strips away chartjunk, highlights comparisons, and uses plain language labels.
Ethical Practices
She maintains transparency about data sources, acknowledges uncertainty, avoids misleading scales, and stresses context so readers can interpret findings responsibly.
Community Impact
Many of her projects target local decision makers, neighborhood advocates, and educators who need reliable visuals to explain policies, budgets, and outcomes.
Project Highlights and Case Studies
Election and Voter Engagement
Visualizations on turnout by precinct, registration deadlines, and candidate issue positions help organizers plan outreach and inform voters efficiently.
Public Health and Equity
Maps and small multiples illustrate disparities in health outcomes, clinic access, and social determinants, guiding resource allocation toward underserved groups.
Education and School Funding
Comparisons of per pupil spending, graduation rates, and program participation expose gaps and support advocacy for more equitable school budgets.
Visualization Best Practices and Tools
Design Principles
She recommends consistent color palettes, pre-ordering categories, removing unnecessary borders, and testing visuals with people unfamiliar with the data.
Tool Stack
Monique Tepe often uses Python with pandas and matplotlib, R with ggplot2, and JavaScript libraries such as D3 or Observable notebooks to prototype and publish results.
Reproducible Workflow
Shared code, version controlled notebooks, and documented datasets allow others to verify findings, update numbers, and build on previous work.
Teaching, Workshops, and Training
Workshops for Nonprofits
She runs sessions where participants clean real local data, build charts from scratch, and receive feedback, turning concepts into actionable skills.
Curriculum Development
Modules on data cleaning, exploratory analysis, and critique of existing graphics prepare students to produce journalism-ready visualizations.
Mentorship and Feedback
One on one mentoring helps learners refine project ideas, iterate on design choices, and prepare public-facing portfolios or reports.
Key Takeaways and Recommended Actions
- Prioritize clarity and accessibility in chart design so that complex topics remain understandable.
- Document data sources and methods to build trust and enable replication.
- Collaborate early with communities to ensure visuals reflect their priorities and language.
- Choose tools that balance reproducibility with the practical constraints of deadlines and skills.
- Iterate on feedback, test visuals with real users, and update projects as new data becomes available.
FAQ
Reader questions
Where can I see Monique Tepe’s visualization projects and code samples?
Explore her portfolio site and GitHub profile for published graphics, interactive projects, and open source notebooks that demonstrate her methods.
Does she offer customized workshops or consulting for newsrooms and community groups?
Yes, she designs tailored sessions focused on practical skills, from data cleaning to storytelling, for organizations looking to strengthen local coverage.
What backgrounds do participants typically have in her workshops?
Attendees range from reporters and editors to community organizers and students, with varying comfort levels in coding and data analysis.
How does she advise beginners who want to build a portfolio in data visualization?
She suggests starting with small, well documented projects, clearly explaining the question, data source, and process, and regularly publishing iterative improvements.