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LinkedIn Learning

Python Statistics Essential Training

via LinkedIn Learning

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Overview

Syllabus

Introduction
  • Being a Python statistics MVP
  • What you should know
  • Using GitHub Codespaces with this course
1. Loading and Cleaning Data
  • Loading data
  • Strings and categories
  • Cleaning numbers
  • Shrinking numbers
  • Challenge: Clean Ames
  • Solution: Clean Ames
2. Exploring and Visualizing
  • Categorical exploration
  • Histograms and distributions
  • Outliers and Z-scores
  • Correlations
  • Scatter plots
  • Visualizing categorical and numerical values
  • Comparing two categoricals
  • Challenge: Explore Ames
  • Solution: Explore Ames
3. Linear Regressions
  • Linear regression
  • Interpreting linear regression models
  • Standardizing values
  • Regression with XGBoost
  • Challenge: Predict Ames
  • Solution: Predict Ames
4. Hypothesis Tests
  • Exploring data
  • Visualizing distributions
  • Running statistical tests
  • Testing for normality
  • Challenge: Checking square footage distributions
  • Solution: Checking square footage distributions
Conclusion
  • Next steps

Taught by

Michele Vallisneri

Reviews

4.6 rating at LinkedIn Learning based on 229 ratings

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