Come join us for a hands-on workshop where we will explore the basics of R, one of the most popular programming languages for data analytics and data science in academia and beyond. Please note that this is a workshop for beginners. If you already have a background in R, please look out for future workshops where we will cover more advanced techniques and explore various R libraries in more detail.
Data @ Rice
Develop skills in organizing, cleaning, analyzing, and visualizing data in this free, hands-on workshop from Data Carpentry. Aimed at social scientists, this workshop will combine short tutorials and hands-on practical exercises.
Who: This free course is aimed at graduate students and other researchers. You don't need to have any previous knowledge.
In this workshop, you will learn how to use Docker to build a basic, interactive app in Plotly and R.
You will learn how to
Launch a Docker environment
Run an app in that environment
In this workshop, you will learn how to build a basic data visualization app using Python.
Basic knowledge of Python and HTML is required. You will be introduced to the Plotly framework for producing interactive data visualizations, and Dash, their package for producing these as lightweight applications that can easily be shared as code or simply as flat HTML pages.
Develop your Python, Git, and Unix skills in a free online workshop taught by experienced instructors from the Carpentries. The workshop will combine short tutorials and hands-on practical exercises.
Who: The course is aimed at graduate students and other researchers. You don't need to have any previous knowledge.
This short course will introduce students to text analysis with an emphasis on learning the technological basics of Python and Jupyter Notebook. After discussing why to learn text mining and text analysis methods, we will explore how to use Python to analyze word frequencies. We will use Constellate to access our dataset.
Drowning in data? Not sure how to organize and back it up? This hands-on, interactive workshop will share tips for effectively organizing, documenting and storing research data. Participants will walk away with ideas for completing a data management plan, naming and organizing files, and safely storing data.
All the primary computing systems & services offered by the Center for Research Computing (CRC) are available to researchers who are working from off campus.
In fact, "remote" access workflows are often the same as best practices for using our resources.
Get a schematic overview, along with some practical examples of how to use the following in concert with one another to multiply your computing power:
Learn how to create lightweight, interactive data visualization apps for medium-sized datasets. This will be useful for introducing students and researchers to basic exploratory data analysis. We will begin with a basic introduction to data frames in Python and R using a basic dataset; then we will see how to render that data as an interactive dashboard in Python and R; finally, we will deploy see what deploying your app to a free public web server in Heroku looks like!