This intake survey will help the Pediatric Stats Support and Data Science team determine how best to support your request for available services.
The BERD Methods Core works with investigators to assess their project’s collaborative needs and help them identify potential collaborators (biostatisticians, data scientists, qualitative scientists, epidemiologists, etc.) with relevant quantitative and qualitative expertise at Duke and partner institutions. In addition, the core builds teams of faculty and staff methodologists (biostatisticians, epidemiologists, bioinformaticists, data scientists, etc.) who work with an interdisciplinary network of clinical and translational investigators.
The mission of the Quantitative Sciences Core (QS) is to provide quantitative support for intramural collaboration and coordination of all AIDS-related research activities at Duke and its partner institutions. By providing quantitative expertise and developing shared computational tools, we aim to enhance the value of the services provided by other CFAR Cores, and to increase the scientific impact of research done by CFAR investigators.
DiscoverData@Duke is a collaborative effort to provide a one-stop shop for Duke investigators to learn about and access a wealth of data resources.
Protected Analytics Computing Environment (PACE) is a highly protected virtual network space that serves as a marketplace where approved users can work with identifiable protected health information.
Estimated Charges: For information about PACE fees, please visit the estimated charges page.
QualCore provides expertise and leadership in qualitative research design, implementation, analysis, and interpretation.
Duke's participation in several Clinical Data Research Networks (CDRNs) makes it easier to conduct multi-site studies by enabling access to well-curated EHR data at all member institutions, including Duke.
EERS makes it easy for innovators and investigators to leverage Duke's EHR and data warehouse resources efficiently and cost-effectively.
Gives Duke researchers access to electronic health data from different sources to generate new insights into health and health care.
R is a programming language that is often used for statistical computing and graphical presentation to analyze and visualize data.
Here, you will find statistics problems similar to those found in popular college textbooks. The R solutions are short, self-contained and require minimal R skill. Most of them are just a few lines in length.
These notes are an introduction to using the statistical software package R for an introductory statistics course. They are meant to accompany an introductory statistics book such as Kitchens “Exploring Statistics”. The goals are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course.
Offers statistical consulting to Duke University faculty, students and staff on research involving statistical methods. Open to Health faculty/staff; however, university staff/faculty are given priority.
Consultations, instructions, co-curricular programming, and research assistance at various stages of the research data lifecycle for faculty, students, and researchers.
One-stop-shop for statistics research support; includes different areas of research, citing resources, data sets & collections, software, and tools.
Individual and class learning modules on a variety of topics for Duke University faculty, staff, and students. The following pathways include different statistics-related courses:
- Research Computing – Duke Systems: Duke-specific research computing track.
- Machine Learning and Data Science: How to use machine learning to analyze data and identify patterns.
- Research Computing – Data Visualization: Learn how to better display your data, or about computing resources at Duke. This track is a product of collaboration with Duke Research Computing and works nicely as a supplement to the Data and Visualizations workshops offered by the library.
- Introduction to Data Science with R Pathway: In this pathway, you will learn how to use the R programming language to perform exploratory data analysis and visualization. Each module is designed for beginners with little to no prior experience in R or data science. By the end of this pathway, you will have the foundational knowledge and skills needed to start using R for data science and to continue developing your skills in this field.
Dec 1, 2023
Dec 4-8, 2023
The workshop will be offered in December as a virtual five-day that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. Click here to register.
Joint NC BERD Seminar: Interaction and effect modification: what are they and how are they different
Dec 6, 2023
190 video series on Statistical Analyses using SPSS ranging from basic to complex analysis.
This free course teaches you how to use R as a calculator and assign variables. You will also learn the basic data types in R. Other courses come with a fee.