COVID19 Data Analysis Using Python Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This hands-on, browser-based guided project provides a practical introduction to data analysis using Python, focusing on real-world datasets related to the COVID-19 pandemic and global life factors. In under two hours, you'll gain experience in importing, cleaning, merging, and visualizing public datasets using core Python libraries. The course is structured to take you from raw data to meaningful visual insights with no setup required, making it ideal for learners with basic Python and Jupyter Notebook familiarity. You’ll work directly in a split-screen environment, applying skills in pandas, Matplotlib, and Seaborn to explore correlations between public health and well-being indicators.
Module 1: COVID-19 Data Analysis Using Python
Estimated time: 1.7 hours
- Import and preprocess COVID-19 datasets from Johns Hopkins repository
- Load and clean World Happiness data for analysis
- Merge datasets using common keys and handle missing values
- Calculate meaningful metrics such as case fatality rate and happiness scores
- Visualize correlations using Seaborn heatmaps and scatter plots
Prerequisites
- Familiarity with basic Python programming
- Experience using Jupyter Notebooks
- Understanding of fundamental data structures like DataFrames
What You'll Be Able to Do After
- Prepare and clean real-world public health and social datasets
- Merge multiple datasets using pandas
- Compute and interpret key analysis metrics
- Visualize relationships between variables using Seaborn
- Draw data-driven insights from epidemiological and socio-economic factors