Detecting Unusual Data

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prerequisites
intermediate Python • basics of statistics (regression analysis) • basics of NumPy and pandas
skills learned
detecting unusual observations • visualizing and analyzing data with Matplotlib, seaborn, and statsmodels
Monica Guimaraes
1 week · 8-10 hours per week · INTERMEDIATE
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catalog / Data Science
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In this liveProject, you’ll build your own Python data library that can detect unusual data. This data can affect regression analysis, and so your library will help spot it before it can negatively affect results. You’ll program functions to detect high-leverage points, outliers, and influential observations. Through the analysis of multiple examples, you'll gain a deep understanding of the kind of problems that can arise with real data.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Monica Guimaraes
Monica Guimaraes has more than fifteen years of experience organizing and teaching undergraduate courses in automata theory, numerical analysis, programming languages, and statistics. Her passion for artificial intelligence goes back to her college years, having done her thesis in automatic-theorem-proving and working later as an AI researcher. She also has extensive experience in software development in corporations.

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
  • Regression Analysis

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