Events

  • From January 1, 2018
  • To March 14, 2023

Intro to Text Analysis

September 9, 2022
10:00 am - 12:00 pm

Computational text analysis (or text mining) is finding, extracting, and analyzing patterns while processing large amounts of text. Attendees will learn an array of computational text analysis concepts, tools, platforms, and resources for getting started with your research. We will be demonstrating beginner-friendly tools like Voyant Tools as well as specific Python packages, including spaCy, scattertext, […]

R Basics: Get Started in RStudio

September 13, 2022
12:00 pm - 1:30 pm

Never worked in R? Get a jump on your business or academic career by exploring this powerful statistical programming language and its popular environment, RStudio. in this workshop, you will: Navigate RStudio​​ Work with objects​​ Use functions and libraries Note: Install R and RStudio before attending, or use our on-site computers. To register, go to this link. Where: Lippincott Library, […]

Python for Humanists

September 13, 2022
2:00 pm - 3:30 pm

This short workshop will introduce humanists to the programming language Python and the numerous possibilities for its use for research in the humanities. No prior programming experience required! Having attended this workshop, participants can expect to be able to: set up a Python coding environment suitable for use with their own computer. recognize potential use […]

What is user experience design, and why should scholars working in digital scholarship care about it? This introduction-level workshop will introduce participants to key concepts in UX design, including wireframing, prototyping, iteration, and user testing. Participants are encouraged (but not required!) to bring ideas or research questions for specific digital projects, if they have them […]

Eager to analyze data? This hands-on introduction will help you organize and format data, supporting your modeling or machine learning workflow. In this workshop, you will: Import and wrangle real-world data​​ Explore data properties​​ Format data for statistical tests and machine-learning Note: Ensure you can run R and RStudio (or use our on-site computers) as well as these packages: dplyr; tidyr; plyr. […]