About Me

My name is Mercy Ogbede, and I am originally from Imo State, Nigeria. My academic background is in education with a major in Government, where I developed an early interest in how information shapes public understanding and policy. I am now a graduate student in the Data Analytics & Computational Social Science (DACSS) program at the University of Massachusetts Amherst. In my studies, I focus on transforming complex datasets into meaningful insights through statistical modeling, visualization, and data-driven storytelling.

Goals

As a developing data analyst, my goal is to transform complex datasets into clear insights that support meaningful decision-making. I am particularly focused on strengthening my ability to communicate analytical results through effective data-driven storytelling, making statistical patterns understandable to both technical and non-technical audiences. I aim to continue improving my skills in modeling, visualization, and interpretation so that I can bridge the gap between data and real-world understanding, ultimately producing analyses that are not only accurate but also compelling, accessible, and useful.

Education

I completed my undergraduate studies at Imo State University in Nigeria, where I earned a Bachelor’s degree in Education with a concentration in Government. That background strengthened my understanding of social systems and public issues, which now shapes how I approach data. I am currently pursuing my M.S. in Data Analytics & Computational Social Science at the University of Massachusetts Amherst, where I am building advanced skills in statistical modeling, machine learning, visualization, and data-driven storytelling.

Experience

I have gained hands-on experience working with a range of analytical tools including Python, R, and SQL, applying them across multiple data-focused projects. My work spans various domains: analyzing CO₂ emissions across U.S. states, modeling housing prices in Dubai, and examining unemployment disparities across demographic groups. I have also completed machine learning projects such as predicting obesity levels based on eating habits and physical conditions. In addition, my experience with text-as-data techniques includes extracting themes from women’s narratives shared on Reddit to better understand health experiences. Across these projects, I have practiced structuring analytical questions, building predictive models, interpreting uncertainty, and crafting clear visual explanations, all essential components of data-driven storytelling.

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