Module 03: Python and Jupyter for Neuroscience

Teaching Deck

Learning Objectives

Set up a reproducible notebook workflow
Load and inspect connectomics data tables
Write basic analysis and visualization code blocks
Document assumptions and outputs for reuse

Session Outcomes

Learners can complete the module capability target.
Learners can produce one evidence-backed artifact.
Learners can state one limitation or uncertainty.

Agenda (60 min)

0-10 min: Frame and model
10-35 min: Guided practice
35-50 min: Debrief and misconception correction
50-60 min: Competency check + exit ticket

Capability Target

Create a reproducible Jupyter notebook that ingests a connectomics dataset slice, performs one analysis, and exports documented outputs.

Concept Focus

Core Workflow

Set environment and dependencies.
Load dataset and validate schema.
Run analysis cell sequence.
Save outputs + metadata.
Re-run from clean kernel.

60-Minute Run-of-Show

00:00-08: notebook anatomy.
08:00-20: setup and loading.
20:00-34: analysis coding sprint.
34:00-46: plotting and export.
46:00-56: clean rerun test.
56:00-60: competency check.

Misconceptions to Watch

Surface and correct one likely misconception during debrief.

Studio Activity

Build a mini notebook with one descriptive statistic and one plot from a sample connectomics table.

Activity Output Checklist

Evidence-linked artifact submitted.
At least one limitation or uncertainty stated.
Revision point captured from feedback.

Assessment Rubric

Minimum: runnable notebook, clear outputs, basic metadata.
Strong: clean structure, robust error handling, repeatable rerun.
Failure: hidden state dependencies, undocumented assumptions.

Exit Ticket

Add one markdown cell documenting input version, processing steps, and output files.

References (Instructor)

Use module references listed on the module page.

Teaching Materials

Module page: /modules/module03/
Slide page: /modules/slides/module03/
Worksheet: /assets/worksheets/module03/module03-activity.md