Suggested Lessons for Teaching Demonstrations

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 Organization in Spreadsheets
  • Data Cleaning with OpenRefine
  • Data Analysis and Visualization in R
  • Data Management with SQL
  • Data Analysis and Visualization in Python
• Data Carpentry: Genomics
  • Introduction to the Command Line
  • Data Wrangling and Processing
  • Introduction to Cloud Computing
• Data Carpentry: Social Sciences
  • Data Organization in Spreadsheets
  • Data Cleaning with OpenRefine
  • Data Analysis and Visualization with R
• Data Carpentry: Geospatial
  • Introduction to R for Geospatial Data
  • Introduction to Raster and Vector Data with R
• Data Carpentry: Astronomy
  • Foundations of Astronomical Data Science
• Data Carpentry: Image Processing
  • Image Processing with Python
• Library Carpentry
  • Library Carpentry: The Shell Lesson
  • Library Carpentry: OpenRefine
  • Library Carpentry: Introduction to Git
• Software Carpentry
  • The Unix Shell
  • Version Control with Git
  • Programming with Python
  • Plotting and Programming in Python
  • R for Reproducible Scientific Analysis
  • Programming with R
  • Using Databases and SQL
  • Automation and Make
  • Version Control with Mercurial
• Lecciones en español
  • La Terminal de Unix
  • Control de versiones con Git
  • R para Análisis Científicos Reproducibles
  • Análisis y visualización de datos usando Python

Data Carpentry: Ecology

Lesson	(For Trainers) Good Starting Points	(For Trainers) Avoid	Notes
Data Organization in Spreadsheets	* Dates as Data * Quality Control	* Introduction * Formatting Data Tables in Spreadsheets * Formatting problems * Exporting data	Episodes listed to avoid have no live coding.
Data Cleaning with OpenRefine	Exploring Data with OpenRefine	any other episode	The intro episode has no live coding. Other episodes have dependencies.
Data Analysis and Visualization in R	* Introduction to R * Starting with Data	* Before We Start * Manipulating Data * Data Visualization with ggplot2 * SQL Databases and R	Before We Start has no live coding. Manipulating Data and Data Visualization with ggplot2 have dependencies. SQL and RSQLite are taught less frequently.
Data Management with SQL	any other episode	Databases using SQL	Live coding starts in the middle of the episode to avoid.
Data Analysis and Visualization in Python	* Short Introduction to Programming * Starting with Data * Indexing, Slicing, and Subsetting * Data Types and Formats * Combining DataFrames with Pandas * Data Workflows and Automation	any other episode	Before we Start has no live coding. Later episodes have dependencies.

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

Lesson	(For Trainers) Good Starting Points	(For Trainers) Avoid	Notes
Library Carpentry: The Shell Lesson	* Navigating the filesystem * Working with files and directories * Automating the tedious with loops * Counting and mining with the shell	What is the shell? * Working with free text	What is the shell? has no live coding. * Working with free text has dependencies
Library Carpentry: OpenRefine	* Importing data into OpenRefine	any other episode	Introduction to OpenRefine has no live coding. Later episodes have dependencies.
Library Carpentry: Introduction to Working with Data (Regular Expressions)	No episodes	Avoid all episodes.	Episodes are built through exercises without an obvious live-coding component that is not explaining the solution to the exercise.
Library Carpentry: Introduction to Git	* Getting started with Git	any other episode	What is Git/GitHub? has no live coding. Later episodes have dependencies.
Library Carpentry: SQL	* Selecting and Sorting Data * Filtering * Ordering and Commenting * Aggregating and Calculating Values * Joins and aliases * Saving queries * Creating tables and modifying data	* Database design * Other database tools * Extra-challenges * Good style

Software Carpentry

Lesson	(For Trainers) Good Starting Points	(For Trainers) Avoid	Notes
The Unix Shell	any other episode	Introducing the Shell	Introducing the Shell has no live coding.
Version Control with Git	* Setting up Git * Creating a Repository * Tracking Changes	any other episode	Automated Version Control has no live coding. Later episodes have dependencies.
Programming with Python	any other episode	Debugging	Debugging has no live coding.
Plotting and Programming in Python	any other episode	Variable Scope Programming Style	Variable Scope is very short and more conceptual/exercises than live coding Programming Style is brief, and mostly concepts/excercises more than live coding.
R for Reproducible Scientific Analysis	any other episode	Splitting and Combining Data Frames with plyr Writing Good Software	The plyr package is largely deprecated. Writing Good Software has no live coding.
Programming with R	* Analyzing Multiple Data Sets * Loops in R * Making Choices	* Creating Functions * Best Practices for Writing R Code * Reading and Writing CSV Files	The episodes to avoid have dependencies.
Using Databases and SQL	any other episode	Data Hygiene	Data Hygiene has no live coding.
Automation and Make	* Introduction * Makefiles	any other episode	Later episodes have dependencies.
Version Control with Mercurial	* Configuring Mercurial * Creating a Repository * Tracking Changes to Files	any other episode	Automated Version Control has no live coding. Later episodes have dependencies.

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.