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  • Data explorations

Information and Meaning

  • Quantitative epsitemology
    • Tutorial: Getting started
    • Discussion questions
  • The value of openness
    • Tutorial: Repositories and version control
    • Discussion questions
    • Exercise 1: Github & Jupyter
  • What is a theory?
    • Discussion questions
  • Models as testable hypotheses
    • Tutorial: Introduction to R, functions, and good coding habits
    • Discussion questions
    • Exercise 2: Coding Habits & Functions
  • Data as objects and architectures
    • Tutorial: Data as Objects and Tidy Data
    • Discussion questions
    • Exercise 3: Data objects
  • Techniques for data cleansing
    • Tutorial: Data Cleansing and the Tidyverse
    • Discussion questions
    • Exercise 4: Data cleansing
  • Visualization as analysis
    • Tutorial: Basics of plotting
    • Discussion questions
    • Exercise 5: Using ggplot
  • Visualization through human eyes
    • Tutorial: More advanced plotting
    • Discussion questions
    • Exercise 6: More plotting options

Knowledge

  • The bias-variance tradeoff
    • Discussion questions
  • Linear models
    • Tutorial: Refresher on working with matrices
    • Discussion questions
    • Exercise 7: Linear models
  • The ordinary least squares solution
    • Tutorial: Refresher for solving oridinary least squares
    • Discussion questions
    • Exercise 8: Linear models, continued
  • Limits and variations of linear regression
    • Tutorial: More on linear models
    • Discussion questions
  • Classifiers
    • Tutorial: Basics classifiers
    • Discussion questions
    • Exercise 9: Classification
  • Mixed effects models
    • Tutorial: Running linear mixed effects models
    • Discussion questions
    • Exercise 10: Mixed effects
  • The beauty of kNN
    • Tutorial: Running kNN models
    • Discussion questions
    • Exercise 11: The beauty of kNN
  • Cross validation
    • Tutorial: Implementing cross validation
    • Discussion questions
    • Exercise 12: Cross validation
  • Resampling methods
    • Tutorial: Boostrap and permutation tests
    • Discussion questions
    • Exercise 13: Resampling methods
  • Mediation and moderation
    • Tutorial: Running mediation and moderation models
    • Discussion questions
    • Exercise 14: Mediation
  • Power analysis via simulations
    • Tutorial: Running basic power analyses
    • Discussion questions
    • Exercise 15: Power analyses
  • Selecting the best model
    • Tutorial: Model selection
    • Discussion questions
    • Exercise 16: Model selection
  • Regularized regression
    • Tutorial: Basic ridge and LASSO models
    • Discussion questions
    • Exercise 17: Regularized regression
  • Principal component methods
    • Tutorial: Basic PCA approaches
    • Discussion questions
    • Exercise 18: Principal component methods

Understanding

  • Reconsidering the p-value
    • Discussion questions
  • Bayes factor
    • Tutorial: Estimating Bayes factors
    • Discussion questions
  • Errors and inferences
    • Discussion questions
  • Telling your data story
    • Discussion questions
  • Theories as social constructs
    • Discussion questions
  • .md

Techniques for data cleansing

Contents

  • Required readings
  • Lecture (Video)
  • Slides (PDF)

Techniques for data cleansing#

=======================

Required readings#

Müller, H., & Freytag, J. C. (2003). Problems, Methods, and Challenges in Data Cleansing. Berlin: HUB-IB-164.

Lecture (Video)#

Techniques for data cleansing

Slides (PDF)#

Techniques for data cleansing

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Exercise 3: Data objects

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Tutorial: Data Cleansing and the Tidyverse
Contents
  • Required readings
  • Lecture (Video)
  • Slides (PDF)

By Timothy Verstynen, Venn Popov, Krista Bond, Charles Wu, Patience Stevens, Amy Sentis, Fiona Horner

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