Suggested Lessons for Teaching Demonstrations
Last updated on 2023-04-04 | Edit this page
The list below shows lessons that have been reviewed for good and bad start points to use for a teaching demonstration. When selecting a lesson for a teaching demonstration, trainees should review this list to ensure it is suitable.
Those who wish may also consult the detailed tables following the list to identify episodes marked “avoid” because they will not be asked to teach those episodes. However, we recommend reviewing the full lesson regardless, as this will help provide context for other start points and is also good preparation for your eventual teaching role!
Note that while a trainee can choose the lesson to teach from, the Trainer leading the demo session will choose the specific episode at the time of the demo session. If you would like to use a lesson that is not listed here, you may contact the Instructor Trainer leading your teaching demonstration or (if no one is listed for your session) email instructor.training@carpentries.org.
If the episode chosen by the trainer is very short and finishes in less than 5 minutes, the trainee can continue on to the next episode in that lesson. They are not required to cover any specific amount of content within the 5 minute period.
- Data Carpentry: Ecology
- Data Carpentry: Genomics
- Data Carpentry: Social Sciences
- Data Carpentry: Geospatial
- Data Carpentry: Astronomy
- Data Carpentry: Image Processing
- Library Carpentry
- Software Carpentry
- Lecciones en español
Data Carpentry: Ecology
Data Carpentry: Genomics
If you are an instructor in training and wish to use lessons from Data Carpentry’s Genomics curriculum for your teaching demo, please read these instructions to be sure you are prepared. You must follow these steps before your teaching demo, or you will be asked to reschedule.
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
Project Organization and Management for Genomics | none | all episodes | Discussion based. No live coding. |
Introduction to the Command Line | all episodes | none | If using Introducing
the Shell begin with cd shell_data
|
Data Wrangling and Processing | * Assessing
Read Quality * Trimming and Filtering * Automating a Variant Calling Workflow |
any other episode | For Assessing
Read Quality begin with
gunzip SRR2584863_1.fastq.gz
|
Introduction to Cloud Computing | * Logging
onto the Cloud * Fine tuning your Cloud Setup |
any other episode | Have trainees teach the version “AWS_UNIX”. |
Data Analysis and Visualization in R | none | all episodes | This lesson is not yet stable. |
Data Carpentry: Social Sciences
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
Data Organization in Spreadsheets | * Dates
as Data * Quality Assurance |
* Introduction * Formatting Data Tables in Spreadsheets * Formatting problems * Exporting data |
Episodes listed to avoid have no live coding. |
Data Cleaning with OpenRefine | Working with OpenRefine | any other episode |
Introduction
has no live coding. Later episodes have dependencies. |
Data Analysis and Visualization with R | any other episode | * Before We
Start * Data Visualization with ggplot2 |
Before
We Start has no live coding. Data Visualization with ggplot2 has dependencies. |
Data Carpentry: Geospatial
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
Project Organization and Management | none | all episodes | This lesson has no live coding. Do not use for teaching demos. |
Introduction to R for Geospatial Data | any episode | none | |
Introduction to Raster and Vector Data with R | * Intro
to Raster Data in R * Reproject Raster Data in R * Work with Multi-Band Rasters in R * Open and Plot Shapefiles in R * Handling Spatial Projection & CRS in R * Convert from a .csv to a Shapefile in R * Raster Time Series Data in R |
any other episode | Many episodes rely on data from previous episodes. Trainees may need extra time to set up their environment. |
Data Carpentry: Astronomy
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
Foundations of Astronomical Data Science | * Basic
Queries * Coordinate Transformations |
any other episode | Later episodes have dependencies. For Basic Queries skip the episode introduction, Query Language, and Using Jupyter sections, and begin with Connecting to Gaia ( from astroquery.gaia import Gaia ) |
Data Carpentry: Image Processing
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
Image Processing with Python | * Working
with skimage * Drawing and Bitwise Operations * Creating Histograms * Thresholding |
any other episode | Other episodes begin with too much explanatory content for a
teaching demo. This lesson is designed to be taught with a JupyterLab environment. |
Library Carpentry
Software Carpentry
Lecciones en español
Lesson | (For Trainers) Good Starting Points | (For Trainers) Avoid | Notes |
---|---|---|---|
La Terminal de Unix | any other episode | Introducción a la Terminal | Introducción a la Terminal no tiene programando en vivo. |
Control de versiones con Git | * Configurando
Git * Creando un repositorio * Rastreando Cambios |
cualquier otro episodio |
Control
Automatizado de Versiones no tiene programando en vivo. Los episodios posteriores tienen dependencias. |
R para Análisis Científicos Reproducibles | cualquier otro episodio | Escribiendo buen software | Escribiendo buen software no tiene programando en vivo. |
Análisis y visualización de datos usando Python | * Breve
introducción a la Programación en Python * Comenzando con datos * Indexación, segmentación y creación de subconjuntos * Data Types and Formats * Combinando DataFrames con Pandas * Flujos de trabajo y automatización |
cualquier otro episodio |
Antes
de comenzar has no live coding. Los episodios posteriores tienen dependencias. |