Immanuel Williams (Cal Poly San Luis Obispo)
Abstract
In today's data-driven environment, the ability to handle real-world data is crucial for those pursuing careers in data science and statistics. Unfortunately, many statistics courses still rely on outdated and prepackaged datasets, which don't prepare students for the messy, unstructured data they'll encounter in real projects and future jobs. This intensive workshop aims to bridge that gap by teaching statistics educators the vital skills of web scraping and API data extraction. Instructors will learn to gather real-time data using R and tidyverse, enabling them to create more engaging and relevant lessons that utilize contemporary datasets.
The workshop is tailored for statistics instructors eager to modernize their teaching methods and better prepare students for practical data challenges. Participants should have intermediate R programming skills and a basic understanding of HTML. They must bring a laptop and have internet access to use CourseKata CKHub Jupyter Notebooks, an essential tool offered in our online resources for effective teaching in statistics and data science.
Each session includes hands-on coding, both individually and in pairs, collaborative discussions, and instructor-led demonstrations focused on practical applications and integrating new content into curriculums. By incorporating skills such as data wrangling and managing unstructured data into their courses, educators can equip students with the proficiencies needed in today’s job market. Moreover, using dynamic, real-world datasets will foster engagement and allow students to tackle projects that resonate with their career goals and interests.