https://github.com/pullisanisatvika/covid-19-death-rate-analysis-with-python

Embark on a data-driven exploration of COVID-19 death rates with this Python-powered tutorial. Through hands-on analysis and visualization, uncover hidden patterns across demographics, locations, and time. Explore the potential of statistical modeling for cautious future predictions, and compare your findings with existing research.
https://github.com/pullisanisatvika/covid-19-death-rate-analysis-with-python

python python3

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Embark on a data-driven exploration of COVID-19 death rates with this Python-powered tutorial. Through hands-on analysis and visualization, uncover hidden patterns across demographics, locations, and time. Explore the potential of statistical modeling for cautious future predictions, and compare your findings with existing research.

• Host: GitHub
• URL: https://github.com/pullisanisatvika/covid-19-death-rate-analysis-with-python
• Owner: pullisanisatvika
• Created: 2024-02-09T14:03:18.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-02-09T14:48:43.000Z (11 months ago)
• Last Synced: 2024-02-09T17:37:25.407Z (11 months ago)
• Topics: python, python3
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# COVID-19 Death Rate Analysis With Python

This data science tutorial delves into the complexities of COVID-19 death rates, utilizing Python for insightful analysis and visualization. You'll join us on a journey to:

# Uncover patterns: 

Explore death rate trends across various demographics, locations, and timeframes using exploratory data analysis and visualization techniques.

Predict the future (cautiously): Investigate statistical modeling approaches (e.g., regression, time series) to potentially forecast future trends, keeping in mind the inherent limitations of such models.

Compare and contrast: Benchmark your findings against existing research and public health data, fostering a comprehensive understanding of the factors influencing COVID-19 mortality.

# Technical Toolkit:

Python: The versatile language powering our data manipulation, analysis, and visualization tasks.

Libraries: Leverage powerful libraries like Pandas, NumPy, Matplotlib, and Seaborn for efficient data wrangling, modeling, and clear visuals.

Open-source data: We'll utilize trusted, publicly available datasets from reputable sources like WHO or national health agencies.

# Learning Outcomes:

Sharpen your Python skills: Gain hands-on experience with essential data science libraries and techniques.

Build critical thinking: Challenge assumptions, interpret data responsibly, and acknowledge limitations.

Boost data literacy: Translate complex data into meaningful insights applicable to real-world public health challenges.

Join us on this data-driven journey and unlock deeper understanding of COVID-19's impact!