Letizia Maukar
Aspiring Data Analyst and Researcher, Recent Computer Science Graduate
Aspiring Data Analyst and Researcher, Recent Computer Science Graduate
Hello, my name is Letizia, and I recently graduated from California State University, Long Beach. I learned programming, collecting, cleaning, and analyzing data, where I was able to have two publications and three poster presentations. Currently, I am building my data analysis skills through a fellowship at CO-OP to gain mentorship and project experience. Outside of that, I work full-time as a Remote Math Tutor at Saga Education, where I lesson plan and facilitate groups of underrepresented high schoolers. And right now, I am looking for a data analyst or data researcher role where I cannot only hone my programming and data skills, but also continue to learn about data analytics. I am open to all opportunities and doing my best in any tasks given, as every opportunity is a learning opportunity!
• Designed and developed an interactive scalable Tableau dashboard by adding visual elements like heat maps and stacked bar charts to meet all internal reporting specifications.
• Conducted equity analysis, visualizing submission and acceptance rates across author demographics (e.g., race, gender, sexuality), and generated filterable data summaries and a final video
presentation outlining strategies to improve journal performance and equity.
• Drove collaborative research efforts for the non-profit Color Compton's grant thesis by utilizing prompt engineering with ChatGPT/Gemini to efficiently source relevant public databases and refine research questions.
• Executed the full data lifecycle, writing Python scripts in Google Colab for data cleaning, analysis, and generating compelling visualizations using Matplotlib and Seaborn for a final presentation to program leadership.
• Designed and developed interactive data visualizations using JavaScript, JSON, and the D3.js library to represent ontological structures, which were then used in an eye-tracking user study to evaluate comprehension and usability.
• Analyzed data collected from the eye-tracking study using Python, Seaborn, Matplotlib, and Google Sheets to identify trends in comprehension and performance; found that tree map and circle packing models significantly
improved user understanding and accuracy in interpreting complex visual data.
Link to publication: A Study of Circle Packing, Tree Map, and Baseline Visualizations in Understanding Ontological Relationships
• Utilized Jupyter Notebook with Python, Pandas, and Seaborn to clean and analyze survey data from 199 families, uncovering behavioral insights on parental perceptions of online risks.
• Assisted in designing a revised Qualtrics survey for additional data collection, followed by a second round of data cleaning, analysis, and detailed documentation to support our paper, poster presentation, and future researchers.
Link to poster presentation: Understanding Children's Safety Through Parental Views of Risks and Strategies
