course_outline.Rmd

---
title: "Course outline"
output: 
  html_document:
    toc: true
    toc_depth: 3
    toc_float:
      collapsed: false
      smooth_scroll: true
---
# Data science
### Introduction to data science
- Introduction
    - Define data science
    - List common tools used in data science

***

# Command line
### Introduction to Unix

- 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

### Applications of command line

- 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
    
***

# R/RStudio
### Introduction to R

- 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

### Data manipulation in R

- Data manipulation tutorial and practice
    - Load tabular data using the [tidyverse](https://www.tidyverse.org/)
    - 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 in R

- 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'*

*** 

# Statistics *Under development*
### Introduction to statistics

- 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

### Statistics in R/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 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
    - 

*** 

# Capstone projects *Under development*
### Metagenomics analysis team project

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

### Microbiome analysis team project

- Focuses on biological interpretation of amplicon sequence data from microbiomes