COVID-19 Global Impact Dashboard

1. Objective The goal of this mini project is to analyze the global spread and impact of COVID-19 using publicly available datasets. We'll use the Pandas library in Python to clean, manipulate, and analyze the data, and present key insights through visualizations and summary statistics.

2. Scope Data cleaning and preprocessing

Trend analysis: Confirmed cases, recoveries, and deaths

Country-wise comparisons

Monthly trend aggregation

Identifying top/bottom affected countries

Basic visualizations (matplotlib/seaborn)

1. Target Audience Data enthusiasts

Healthcare researchers

Policy makers

General public interested in COVID-19 trends

📅 Project Plan (1-2 Days) Day Task Tools Day 1: Data Handling & EDA
Morning Load dataset, inspect schema, handle missing values Pandas Afternoon Clean data (date format, groupby, aggregations) Pandas Evening Exploratory Data Analysis (EDA) Pandas, matplotlib/seaborn

| Day 2: Analysis & Reporting | | Morning | Time series trends (daily, monthly cases) | Pandas, matplotlib | | Afternoon | Country-wise comparison and rankings | Pandas | | Evening | Visualizations and final dashboard | Seaborn, Matplotlib | | Final | Documentation, summary, and GitHub push | Markdown, GitHub |

📦 Dataset Use the Johns Hopkins COVID-19 Dataset or Kaggle datasets such as:

COVID-19 World Dataset

Built With

• scratch