- Introduction
- Define data science
- List common tools used in data science

- Introduction
- Define command line
- Describe several advantages to using command line

- Download instructions
- Provides instructions for download and install of Unix terminals for Mac, Linux, and Windows

- Unix navigation tutorial and practice
- Define parts of the terminal
- Use Unix commands to navigate your computer including pwd, ls, man/help, and cd

- Unix manipulation tutorial and practice
- Use Unix commands to manipulate files including mkdir, cp, mv, and rm
- Apply equivalent file paths in Unix commands
- Define best practices for directory and file names

- BLAST tutorial and practice
- Complete nucleotide BLAST of a large sequencing dataset using command line tools

- Git tutorial and practice
- Enact version control on a text file using Git command line tools

- GitHub tutorial and practice
- Share and modify a version controlled file using GitHub

- Introduction
- Describe general uses for R
- List several advantages to using R and RStudio

- Download instructions
- Provides instructions for download and install of R and RStudio

- RStudio tutorial
- Navigate the RStudio software including key shortcuts, projects, packages, and help
- All of our R tutorials and practice are implemented in RStudio so we
**strongly recommend**that this tutorial be included with all R curriculum

- Base R tutorial and practice
- Execute commands in base R to:
- Load tabular data
- Access columns and rows within a data frame
- Perform basic calculations on tabular data
- Subset a data frame

- Execute commands in base R to:

- Data manipulation tutorial and practice
- Load tabular data using the tidyverse
- Subset and clean data in
`dplyr`

(filter, select, rename, arrange, mutate) - Summarize data in
`dplyr`

(group_by, summarize) - Transform data frames using
`tidyr`

(gather, spread) and`dplyr`

(*_join) - Link multiple tidyverse functions using pipes
`%>%`

- Data visualization tutorial and practice
- Define the grammar of graphics
- Create scatterplots using the
`ggplot2`

package - Customize plot color, shape, axes, scales, and other attributes
- Represent subsets of data using facets
*Recommend first completing ‘Data manipulation in R’*

- Introduction
- Identify and distinguish between a population and a sample, and between parameters and statistics
- Define “p-value”" and interpret its meaning
- Identify factors that influence statistical test selection

- Download instructions
- Provides instructions for download and install of R and RStudio

- RStudio tutorial
- Navigate the RStudio software including key shortcuts, projects, packages, and help
- All of our statistics tutorials and practice are implemented in RStudio so we
**strongly recommend**that this tutorial be included with all R curriculum

*t*-tests- Analysis of Variance (ANOVA)
- Linear regression

- Focuses on pipeline construction and biological interpretation of metagenomic sequence data from microbiomes

- Focuses on biological interpretation of amplicon sequence data from microbiomes