Project Brief

Create an interactive data visualization dashboard analyzing a real-world dataset. Your dashboard must include data preprocessing and cleaning workflow, at least 5 different visualization types (charts, graphs, maps), interactive filtering and drill-down capabilities, statistical summaries and key performance indicators, and a written report explaining design decisions and insights discovered. Use industry-standard tools such as Tableau, Power BI, or Python libraries (matplotlib, seaborn, plotly).

My Work

Reflection

This data visualization project was comprehensive work analyzing global climate change indicators. I examined temperature anomalies, CO2 emissions, and sea level data to create an interactive dashboard revealing environmental trends and patterns. The project deepened my understanding of visual analytics beyond creating charts. Selecting appropriate visualization types required careful consideration of data characteristics and communication goals. I learned that effective visualizations tell stories and reduce cognitive load for decision-makers. Implementing interactivity proved challenging but rewarding. Adding filters, drill-down capabilities, and tooltips transformed static charts into exploratory tools. I learned that interactivity empowers users to discover insights aligned with their questions and interests. The data preparation phase consumed more time than anticipated. Cleaning inconsistent data, handling missing values, and aggregating time series required attention to detail. I learned that visualization quality depends on data quality, making preprocessing critical to success. Design decisions required balancing aesthetics and functionality. Choosing color schemes, layouts, and chart types involved understanding visual perception principles and accessibility guidelines. This project demonstrated that data visualization is both technical skill and communication art, bridging data science and storytelling.