Demographic Data Analysis

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prerequisites
intermediate Python • basics of statistics (regression analysis) • basics of NumPy and pandas
skills learned
reading, cleaning, and merging data with pandas • exploring data for trends and distributions • doing data visualizations with Matplotlib and Seaborn
Monica Guimaraes
1 week · 8-10 hours per week · INTERMEDIATE
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Demographic Data Analysis
Look inside
In this liveProject, you’ll use the Python data ecosystem to explore how demographic data affects American life expectancy. You’ll use pandas and NumPy to clean and merge a newly collected data set Matplotlib, and seaborn to create a variety of plots that showcase the distribution of the data and expose the relationships between the variables.
This project is a part of the series Regression for Life Expectancy Analysis.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Monica Guimaraes
Monica E. Guimaraes is a Healthcare Prompt Engineer at Centific, where she designs and evaluates LLM prompt frameworks for AI-generated clinical documentation. She previously led the corporate data warehouse project at Banco Rio de la Plata (Grupo Santander), conducted graph analytics research at Fraunhofer IOSB, and lectured in statistics at Aalen University, where she earned her MSc in Machine Learning and Data Analytics. For Manning Publications, she has developed liveProjects and video walkthroughs, including the ECLSS Intelligence Series, which teaches production-grade RAG and LangGraph agent development through Mars life-support scenarios.
Monica Guimaraes

prerequisites

This liveProject is for confident Python programmers. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of Python data analysis

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