Data Science Training Program
The scripting language R is considered one of the most powerful languages for quantitative analysis, statistics, and graphics. This workshop will help you get started using R to analyze your datasets and create graphs for visualization. You'll do hands-on exercises to demystify data analysis using R. This workshop is designed for those who have no background whatsoever in programming/R.
Part 1: The R language and RStudio
- What is R and why should you use it?
- The RStudio interface
- Troubleshooting error messages
- Types & data structures
- Math and logic operations
- Functions and packages
- Reading data into R
Part 2: Hands-on Data Analysis in R
- Filtering and reformating data
- Exploring data (Basic summaries such as mean, median, etc.)
- Plotting data
- R markdown report generation
Bring your laptop with RStudio and R installed.
Novice: This is an introductory workshop in the R Scripting series with no prerequisites. No background in statistics or computing is necessary, and no prior experience with programming or R/RStudio is required for this course. Absolute beginners are especially welcome!
Visit the workshop site for more details and materials.
AudienceGladstone and UCSF
The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.
Diversity, Equity, and Inclusion
At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.
We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.