Analyze global CO₂ emissions alongside GDP and population data. You’ll clean, explore, and visualize the data, then build an interactive dashboard in Dash. Your final presentation should highlight key patterns, tools used, and insights discovered.
Deliverables:
- Dataset Used: The student will use a publicly available dataset on global CO₂ emissions by country and year, along with GDP and population data for context. (e.g., Our World in Data)
- Exploratory Data Analysis (EDA): Clean and preprocess the data to handle missing values and inconsistent formats. Use correlation analysis and group-by techniques to understand trends and relationships between emissions, GDP, and population over time.
- Visualizations: Create insightful charts such as time series plots of emissions by continent, correlation heatmaps between GDP and emissions, and a bar chart of top polluting countries. Use matplotlib, seaborn, and plotly for varied visual appeal.
- Dashboard Implementation: Build a responsive, interactive dashboard using Dash, where users can filter data by region, select time ranges, and visualize the top emitters or GDP/emission ratios over time.
- Findings and Patterns: Present observations such as which countries have decoupled GDP growth from emissions, regional emission trends, or anomalies. Emphasize tools used (Pandas, Plotly, Dash, correlation analysis, apply() and lambda functions, etc.) and how they were used to derive insights.