R for Data Science

#### 95-778

Units: 6

Description: This course introduces students to advanced features in R to prepare them for a career in Data Science. Students will be exposed to the Tidyverse framework, the advanced method of manipulating data within R as well as the data science lifecycle that is encompassed within RStudio. The course will cover data wrangling, advanced data visualization (including d3), and the modeling paradigm of Machine Learning. These are the skills that allow data science to happen, and students will get the practices by doing each of these things with R. Students will also be exposed to daily routines of data science and will be given problems from industry to solve. Further, the course will expose students to data science application lifecycle using Git for version control and GitLab for communication and collaboration of data science projects.

Learning Outcomes: By the end of the class, students learn to:
- Use [RStudio](http://www.rstudio.com) projects and advanced features.
- Use Git for version control.
- Use [GitLab](http://www.gitlab.com) for collaboration.
- Use R to perform modeling.
- Use RStudio Shiny to present your work.
- Import and export data from various sources.
- Perform data wrangling.
- Produce visualizations with ggplot2 and other libraries.
- Perform machine learning models in R.
- Use R Notebook to perform data science analysis.
- Create simple Shiny dashboards.

Prerequisites: 90-711 or 95-796

Syllabus: 95-778_R_for_Data_Science_Syllabus_F18.pdf