Plotting with Seaborn

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prerequisites
basic to intermediate Python 3 programming skills • familiarity with Anaconda Python • familiarity with Jupyter Notebook • familiarity with Matplotlib
skills learned
visualize relations in categorical data • visualize relations in numerical data • visualize pairwise relationships
Nimrita Koul
1 week · 6-8 hours per week · BEGINNER
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You’re a data analyst at Global Consensus Bureau, and your manager has asked you to determine how factors like education, hours worked per week, gender, and age relate to citizens’ incomes. Using pandas, you’ll pre-process data from a survey conducted by the U.S. Census Bureau. Then, you’ll create different charts to identify statistical relationships in data, using Python’s seaborn library, which provides custom functions for visual elements. When you’re done, you’ll know how to use seaborn to create visually appealing charts that reveal accurate and useful insights.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Nimrita Koul

Nimrita Koul is an assistant professor of Computer Science and Machine Learning from Bangalore, India. She holds a Phd in Computer Science with 16 years of experience in teaching Computer Science courses to University undergraduates. Nimrita is a principal investigator for three funded research projects in machine learning. She has designed and demonstrated about 20 data science and machine learning projects to more than 650 students. Nimrita has spoken at multiple international conferences and events.

prerequisites

This liveProject is for data analysts, research scholars, and software engineers who would like to learn how to enhance their plotting skillset with Python’s seaborn library. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Intermediate Python programming skills (variables, functions, loops)
  • Familiarity with Anaconda Python Distribution and Jupyter Notebook
  • Familiarity with Matplotlib (create and customize plots using object-oriented Matplotlib interface)
  • Familiarity with pandas
  • Use Jupyter Notebook for Python
TECHNIQUES
  • Download and install Anaconda Distribution of Python and Jupyter Notebook
  • Install Python packages using conda or pip package managers

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