Browsing by Author "Qiu, Alison"

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    Data Analytics Approach to Addressing Children's Mental Health in the Pandemic Era
    (Rice University, 2023) Qiu, Alison
    This is a presentation of the project "Data Analytics Approach to Addressing Children's Mental Health in the Pandemic Era" which was presented by its author at the 2023 Shapiro Showcase event at Rice University. The project employed a code-based "data pipeline" to process bulk healthcare data and to yield usable statistical results. In this case, the dataset is a collection of medical wellbeing surveys taken during the COVID 19 pandemic, with the goal of extracting statistically relevant predictors for adult and child mental health scores.
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    Fondren Library Data Repository for Data Science Education and Experiential Learning
    (Rice University, 2023-06-15) Xiong, Anna; Chen, Su; Barber, Catherine R.; Sun, Nik; Qiu, Alison; Li, Tina
    This project piloted a process for creating a repository of interesting, real-world government datasets that are easy to access, beginner-friendly, and suitable for educational use, particularly in data science. The project resulted in three sub-projects, each of which uses one or more open government datasets to demonstrate the data science pipeline. The first sub-project (1_mental_health_project) used the U.S. Census Bureau's Household Pulse survey to explore correlates of mental health during the COVID-19 pandemic. The second sub-project (2_education_demographics_project) used the National Center for Education Statistics' National Household Education Survey and Common Core of Data along with the Texas Education Agency's graduation data to explore relationships among educational outcomes, student and family demographic variables, and county demographic diversity within the 12th grader population. The third sub-project (3_economics_employment_project) used the U.S. Census Bureau's Current Population Survey and a wide range of financial data (COVID-related spending, Medicaid spending, GDP, and minimum wage) to explore the relationship beteen government fiscal relief measures and employment during recessions. The three sub-project folders include clean datasets, code for cleaning and analyzing the data, and interpretation of the results. These materials are suitable for a range of learners within data science, including both novices and those with advanced statistical skills.