Chapter 3 Course Schedule

3.1 Weeks 1-2

  1. Data organization and management
    • best practices, reproducibility, etc.
  2. Basic programming fundamentals for data curation
    • The Unix environment and fundamental commands
    • Formatting and manipulating tabular text files from the terminal
  3. Introduction to R and Rstudio
    • Installation/Updates
    • R object types and assignment
  4. Practice with R objects
    • vectors, matrices, data frames, etc.
  5. Applying core programming fundamentals in R
    • vectorized operations
    • replicate, apply family, ifelse, for loops, etc.

3.2 Week 3

  1. Plotting/visualizing data as a means of exploration
    • Different plot types
    • Scale, transformations, etc.
  2. Fundamentals of plotting in base R
    • par
    • using palettes, points, sizes, etc. to convey information
    • axes and labels
  3. R markdown

3.3 Week 4

  1. Population parameters, samples, and sampling distributions

    • Central Limit Theorem and the normal dist.
    • Mean and st. dev.

  2. Probability and probability distributions

  3. Calculating summary statistics

    • Other common summary statistics (quantiles, etc.)

3.4 Week 5

  1. Parameter estimation
    • Simulating data sets with known parameters
    • Revisit probability distributions
  2. Uncertainty in estimation
    • Parametric and nonparametric approaches to uncertainty

3.5 Week 6

  1. Experimental design
    • lexicon
    • considering sources of variance
    • types of variables (categorical, ordinal, rational)
    • confounding variables
  2. Frequentist hypothesis testing
    • error types
    • p-values
    • degrees of freedom
    • statistical power
    • multiple testing problem

3.6 Week 7

  1. Comparing means between groups
    • Student’s t-test
  2. Bootstrapping and randomization to compare means

3.7 Week 8

  1. Relationships between quantitative variables
    • correlation and covariance
  2. Simple linear regression
    • residuals and least squares
    • fitting linear regression models

3.8 Week 9

  1. Analysis of variance
    • Table components and test statistics
  2. General linear models in R
    • Model formulae
    • Interpretation of summary output
  3. More complex ANOVA frameworks
    • Nested models
    • Factorial models

3.9 Week 10

  1. Frequency-based statistical tests
    • Chi-squared tests
    • Contingency tables and tests of independence
  2. Brief introduction to generalized linear models (time permitting)
    • logistic regression