This five-week, hands-on course introduces students and professionals to R — one of the most widely used languages in data science, statistics, and research. Through guided practice and real-world examples, students learn to write, debug, and organize R code using the RStudio environment, work with datasets, build visualizations, and run analyses on high-performance computing (HPC) infrastructure.

Whether you are a researcher, analyst, or aspiring data scientist, this course gives you a practical foundation in R that you can immediately apply to your own data and projects. It is also a required step toward the HPE DSI Micro-credential in Advanced Data Science.

R Ecosystem and Tools

• Environment: R (base language), RStudio IDE
• Data Import: readr, readxl, data.table
• Data Manipulation: dplyr, tidyr (part of tidyverse)
• Visualization: ggplot2, plotly
• Reporting: R Markdown, knitr
• Packages & Utilities: tidyverse
• HPC & Computing: HPE DSI HPC Clusters (remote R execution)

Disclaimer: The tools, technologies, and learning outcomes listed in this syllabus are subject to change based on industry developments, software availability, or course improvements. Students will be notified in advance of any adjustments.

Learning Outcomes

By the end of this course, students and professionals will be able to:

1. Getting Comfortable with R and RStudio
   • Set up and navigate the RStudio environment confidently, understanding how the console, script editor, and file panes work together.
   • Write, run, and save R scripts in a clean, organized, and reproducible way.
2. Reading and Working with Data
   • Load datasets into R from a variety of sources, including CSV files, Excel spreadsheets, and online repositories.
   • Understand different data types and structures in R — such as vectors, data frames, and lists — and know when to use each.
3. Using R Packages
   • Find, install, and load R packages to extend what R can do out of the box.
   • Use the tidyverse collection of packages to write cleaner, more readable data analysis code.
4. Writing R Functions and Clean Code
   • Write your own R functions to automate repetitive tasks and make your code reusable across projects.
   • Debug R code effectively — understand error messages, identify problems, and fix them without frustration.
   • Add meaningful comments to your code so others (and your future self) can understand what it does.
5. Visualizing Data in R
   • Create a range of charts and graphs using ggplot2 — from bar charts and histograms to scatter plots and line graphs.
   • Customize and style visualizations to communicate findings clearly to both technical and non-technical audiences.
6. Running R on HPC Clusters
   • Understand what high-performance computing (HPC) is and why it matters for large-scale data analysis.
   • Submit and run R scripts on the HPE DSI HPC clusters, opening the door to more powerful and scalable data work.
7. Building and Evaluating Statistical & Predictive Models
   • Build statistical models in R to uncover patterns, test hypotheses, and understand relationships within your data.
   • Develop predictive models that estimate outcomes and support data-driven decisions in real-world scenarios.
   • Evaluate model performance using appropriate metrics — so you know not just what the model predicts, but how much you can trust it.
   • Compare multiple models and select the best one based on accuracy, simplicity, and how well it generalizes beyond the training data.