The Center for Applied Data Science (CADS) aims to be a pivotal resource for both students and faculty at the School of Nursing, guiding them towards achieving exceptional research outcomes. We provide essential data science support that is critical for conducting rigorous, impactful research, thereby significantly contributing to the expansion and enrichment of the School of Nursing’s research portfolio. Our overarching goal is to produce research that is not only academically rigorous but also has a tangible, positive impact on healthcare outcomes. We are dedicated to nurturing a culture of excellent, innovative data science, ensuring that the School of Nursing remains at the cutting edge of healthcare research and education.
Contact Information: For questions please email us at srkesler@austin.utexas.edu.
Our Objectives
Our mission is to empower both faculty and students through the provision of advanced data analysis tools and robust data science education. We strive to enhance the quality of research by fostering a culture of rigorous, ethical, and innovative inquiry that addresses pressing health challenges.
Recognizing the critical role of funding in academic research, we are committed to augmenting the success rates in securing research grants. This is achieved by offering specialized support in the data analysis aspects of grant proposal development, data collection and management. Our aim is to not only secure funding resources, but also to ensure their efficient and impactful utilization.
Central to our mission is the advancement of data science education. By integrating cutting-edge data analysis techniques, predictive modeling, and machine learning into the curriculum, we provide a strong foundation for students and faculty to excel in data-driven research. Our approach is rooted in the principles of precision medicine, ensuring that our community is well-equipped to generate actionable insights and foster data-driven decision-making.
- Strengthen Research Quality: Provide robust data analysis and statistical services to validate the rigor of ongoing and future research.
- Enhance Funding Success: Offer specialized support for data analysis plan preparation and methodological soundness.
- Data Science Support: Offer consultative services to students and faculty regarding data-related aspects of programmatic research development.
Our Services
Data Analysis and Modeling
- Expert consultation on research design, inferential statistics, and data visualization techniques.
- Assistance with creating and validating predictive models, with an emphasis on addressing challenges like algorithmic bias and statistical assumptions.
- Support for implementing advanced methods including utilization of high dimensional (“big”) data, machine learning, and Bayesian statistical modeling.
Grant Proposal Support
- Assistance in preparing the data analysis plan.
- Power analysis for sample size estimation.
- Vetting of data analysis methods to strengthen the competitiveness of the proposal.
Dissertation Support
- Expert advice on optimizing research design for methodological rigor and feasibility.
- Customized consultation to guide students through the analytical aspects of their dissertation work.
Data Science Education
Tutorials and Research Tools
All files below are in PDF format.
Our Team
Shelli Kesler, PhD
Director
Email: srkesler@austin.utexas.edu
Dr. Kesler's Expertise: linear mixed models, growth mixture models, survival analysis, Bayesian networks, longitudinal SEMs, graph theoretical analysis, and machine learning applications (clustering, random forest, support vector machine, regularized regression). She provides data and power analysis plans for grant applications and serves as a statistical co-investigator for these projects. Email to schedule an appointment.
Jasmine Zeng, MS
Data Scientist
Email: jasminez@utexas.edu
Jasmine Zeng's Expertise: advanced inferential statistics (SEM, nonparametric tests), survival and longitudinal analyses, missing data imputation, machine learning, power analysis, data analysis plan for grant applications, data visualization, data cleaning and management (REDCap), statistical software instruction (R, SAS, SPSS, Python)
Oscar Franco Rocha, BSN, RN
Graduate Research Assistant
Email: oscar.francorocha@austin.utexas.edu
Oscar Franco Rocha's Expertise: descriptive statistics and tables for publications, basic inferential statistics (correlation, t-test), data cleaning, data management (REDCap, Qualtrics)
Emma Cho
Graduate Research Assistant
Email: emma.cho@utexas.edu
Emma Cho's Expertise: descriptive statistics and tables for publications, basic inferential statistics (correlation, t-test), data cleaning