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STATS 3DA3

DATA SCIENCE METHODS

Undergraduate

Winter 2025 · C01

Topics

  • Introduction to data science
  • Data transformation, integration
  • Set up the computing environment, review Python, Homework assignment template and submission
  • Data visualization (grammar of graphics, one variable EDA)
  • Data visualization II (covariance analysis)
  • Data visualization application
  • Data visualization III (qualitative variables, text mining)
  • Interactive data visualization
  • Data visualization application (Shiny)
  • Introduction to statistical learning, function fitting
  • KNN classification and regression
  • KNN application
  • Cross-validation and model selection
  • Introduction to tree-based methods, classification tree Regression tree
  • CART application
  • The bootstrap method for bagging Bagging and Random forests
  • Boosting
  • Tree ensemble methods application
  • PCA Dimensionality reduction
  • Clustering I
  • PCA and clustering examples
  • Clustering II
  • Transformations, Missing values, Outliers and error analysis,
  • How do we use Git and GitHub with clustering examples
  • Linear regression
  • Logistic regression
  • Generalized linear model application

Readings

  • Python Data Science Handbook
    Jake VanderPlas · Required
    O'Reilly Media, Inc.
  • An Introduction to Statistical Learning with Applications in Python
    Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor · 2023 · 2nd ed. · Required
    Springer
  • Software
    Required
  • Elements of Statistical Learning
    Trevor Hastie, Robert Tibshirani, Jerome Friedman · Recommended / optional
    Springer
  • Getting Started with Git and GitHub in your Python Project
    Recommended / optional
  • 50 Years of Data Science Top 10 Ideas in Statistics That Have Powered the AI Revolution ISLP Chapter 1
    See source
  • PDS: Chapter 3 How to share data with a statistician Tidy Data
    See source
  • PDS: Chapter 1, 2
    See source
  • PDS Chapter 4
    See source
  • PDS Chapter 4 Applied Text Analysis with Python by Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda – access through McMaster library
    See source
  • Shiny for Python
    See source
  • ISLP Chapter 2 and examples
    See source
  • ISLP Sections 3.2, 3.5, 4.7.6
    See source
  • ISLP Sections 5.1, 5.3
    See source
  • ISLP Sections 8.1.2-8.1.4, 8.3 Classification and Regression Trees ISLP Sections 8.8.1, 8.3
    See source
  • ISLP Section 5.2, 5.3.4 ISLP Sections 8.2.1-8.2.2, 8.2.5, 8.3
    See source
  • ISLP Sections 8.2.3-8.2.4, 8.3
    See source
  • ISLP Sections 12.2, 12.5
    See source
  • ISLP Section 12.4, 12.5
    See source
  • ISLP Sections 3.1 – 3.3, 3.6
    See source
  • ISLP Sections 4.3.4 - 4.3.5, 4.5 -4.7.2
    See source
  • ISLP Pages 152-156 https://en.wikipedia.org/wiki/F-score
    See source
  • ISLP Sections 6.1, 6.5.1
    See source
  • https://textbooks.mcmaster.ca ↗
Sources