4  Course calendar

4.1 Calendar overview

Use the calendar table below to see what’s coming each week; simply click a week (first column link) to jump to full details, and then expand sections to see what to do before, during, and after class. This calendar may change slightly as we progress through the term. You will receive immediate notification of any and all changes.

Note

All Milestone Assignments due by end of day on Friday.

Week Topics In-class exercise Milestone assignment (due Friday) Stress level Grade weight (%)
Week 1
19-Jan-2026		 Course intro; RStudio setup; AI guardrails Project scoping discussion — Very low 0
Week 2
26-Jan-2026		 File structure; reproducible workflows; AI logs File audit walkthrough Analysis Concept Note Low 5
Week 3
02-Feb-2026		 Data exploration; measurement & uncertainty Exploratory data analysis walkthrough — Very low 0
Week 4
09-Feb-2026		 GLMs (theory + practice) GLM exercise Data Readiness Note Low 10
Week 5
16-Feb-2026		 GLMs Model comparison exercise — Low 5
Week 6
23-Feb-2026		 GLMMs GLMM exercise — Moderate 15
Week 7
02-Mar-2026		 GAMs GAM exercise Working Model (draft) Moderate 0
Week 8
09-Mar-2026		 GAMMs; spatial & temporal heterogeneity Model refinement exercise Working Model (final lock) Moderate 15
Week 9
16-Mar-2026		 Student Spring Break — — None 0
Week 10
23-Mar-2026		 Structural Causal Modeling (SCM) Causal diagram critique — Low 10
Week 11
30-Mar-2026		 Instructor Spring Break — — None 0
Week 12
06-Apr-2026		 Prediction & uncertainty Prediction checks Interpretation Memo Low 10
Week 13
13-Apr-2026		 Synthesis & justification Peer + AI review — Moderate 0
Week 14
20-Apr-2026		 Tables, figures, reporting standards Table/figure workshop Results Section Low 10
Week 15
27-Apr-2026		 Writing Results sections Draft clinic Full Draft Moderate 15
Week 16
04-May-2026		 Reflection & closure Course wrap-up Revision Plan (not executed) None 5

4.2 Calendar edit history

Removed AIC model selection from Week 05.

4.3 Weekly Information

Click to expand sections below to see expectations before, during, and after class.

Topics: Course introduction; RStudio setup; AI guardrails
Stress level: Very low
Grade weight: 0%

Before class

  • Absolutely nothing.

In class

  • Course overview and expectations
  • Review syllabus
  • Read AI policy and documentation expectations
  • Project scoping discussion

After class

  • No submission due
  • Install R and RStudio
  • Read AI use and documentation expectations
  • Begin gathering your dataset files

Topics: File structure; reproducible workflows; AI logs
Stress level: Low
Grade weight: 5%

Before class

  • Review example project directory structures
  • Read guidance on reproducible workflows

In class

  • File audit walkthrough
  • Discussion of AI interaction logging

After class

  • Submit: Analysis Concept Note

Topics: Data exploration; measurement & uncertainty
Stress level: Very low
Grade weight: 5%

Before class

  • Skim examples of exploratory data analysis (EDA)
  • Review notes on uncertainty and measurement error

In class

  • Exploratory data analysis walkthrough
  • Discussion of data limitations and noise

After class

  • No submission due
  • Begin informal exploration of your own data

Topics: GLMs (theory + practice)
Stress level: Low
Grade weight: 10%

Before class

  • Read GLM conceptual overview
  • Review examples of common link functions

In class

  • GLM exercise
  • Translating scientific questions into model form

After class

  • Submit: Data Readiness Note

Topics: GLMs; AIC & information theory
Stress level: Low
Grade weight: 5%

Before class

  • Review information-theoretic model comparison
  • Read example AIC workflows

In class

  • Model comparison exercise
  • Interpreting relative support

After class

  • No submission due
  • Refine candidate model sets

Topics: GLMMs; effective sample size
Stress level: Moderate
Grade weight: 15%

Before class

  • Read GLMM overview
  • Review motivation for random effects

In class

  • GLMM exercise
  • Discussion of pseudoreplication and effective sample size

After class

  • No submission due
  • Begin transitioning models to mixed frameworks where appropriate

Topics: GAMs
Stress level: Moderate
Grade weight: 0%

Before class

  • Review motivation for smooth terms
  • Read conceptual introduction to GAMs

In class

  • GAM exercise
  • Interpreting smooths vs parametric effects

After class

  • Submit: Working Model (draft)

Topics: GAMMs; spatial & temporal heterogeneity
Stress level: Moderate
Grade weight: 15%

Before class

  • Review examples of spatial and temporal structure
  • Read GAMM case studies

In class

  • Model refinement exercise
  • Diagnosing remaining structure in residuals

After class

  • Submit: Working Model (final lock)

Topics: Student Spring Break
Stress level: None
Grade weight: 0%

Notes

  • No class
  • No assignments due
  • Recommended: rest, catch up, or light review if needed

Topics: Structural Causal Modeling (SCM)
Stress level: Low
Grade weight: 10%

Before class

  • Read SCM overview and motivation
  • Review example causal diagrams

In class

  • Causal diagram critique
  • Discussion of assumptions and identification

After class

  • No submission due
  • Consider how SCM reframes your modeling decisions

Topics: Instructor Spring Break
Stress level: None
Grade weight: 0%

Notes

  • No class
  • No assignments due

Topics: Prediction & uncertainty
Stress level: Low
Grade weight: 10%

Before class

  • Review prediction vs inference distinctions
  • Read examples of uncertainty communication

In class

  • Prediction checks
  • Evaluating extrapolation risk

After class

  • Submit: Interpretation Memo

Topics: Synthesis & justification
Stress level: Moderate
Grade weight: 0%

Before class

  • Review synthesis examples from prior studies

In class

  • Peer + AI review
  • Justifying analytical decisions

After class

  • No submission due
  • Prepare figures and tables for results

Topics: Tables, figures, reporting standards
Stress level: Low
Grade weight: 10%

Before class

  • Review reporting guidelines
  • Examine good and bad figure examples

In class

  • Table/figure workshop
  • Emphasis on clarity and restraint

After class

  • Submit: Results Section

Topics: Writing Results sections
Stress level: Moderate
Grade weight: 15%

Before class

  • Review example Results sections
  • Reflect on narrative flow

In class

  • Draft clinic
  • Focused feedback on structure and interpretation

After class

  • Submit: Full Draft

Topics: Reflection & closure
Stress level: None
Grade weight: 5%

Before class

  • Review feedback on full draft

In class

  • Course wrap-up
  • Reflection on analytical growth

After class

  • Submit: Revision Plan (not executed)