Hannah Kurzweil (Data Science 4 Everyone)
Abstract
Transform your statistics classroom by leveraging the analytical potential of TikTok data with students' inherent love of social media! This interactive session demonstrates how to teach fundamental statistical concepts through the lens of social media engagement metrics, making statistical analysis immediately relevant to students' daily experiences.
Participants will explore a complete, classroom-ready lesson that integrates exploratory data analysis, visualization, and statistical inference using real TikTok data. Through hands-on activities, you'll learn to guide students through time series analysis, engagement metrics visualization, and correlation studies using Python's data science tools.
The session demonstrates how to build useful statistical models while teaching sampling, correlation, and multivariate analysis in a context that resonates with students. Activities include creating box plots for posting time analysis, exploring engagement patterns across content categories, and building simple predictive models for viral content success. You'll leave with ready-to-implement lesson materials, including Python notebooks, datasets, and assessment strategies.
Intended for statistics instructors teaching introductory courses at the undergraduate level who want to modernize their curriculum with real-world applications. While basic familiarity with introductory statistics concepts is expected, no prior Python programming experience is required. Participants should bring laptops with web browsers to access Google Colab; all necessary Python notebooks and datasets will be provided. Discover how social media analysis can transform abstract statistical concepts into engaging, practical learning experiences that students will actually remember!