Upcoming Events

episummer Introduction to Biostatistics Course Description The course will give an overview of the basic tools for the collection, analysis, and presentation of data in all areas of public health. The topics covered will include descriptive statistics; hypothesis testing; methods for comparison of discrete and continuous data including ANOVA, t-test, correlation, chi-squared analysis, linear and logistic regression, and non-parametric approaches. This course will provide a foundation for the skill-building courses and focused epidemiologic courses.  

episummer Spatial Epidemiology Course Description Spatial epidemiology is the study of geographic distributions and determinants of health in populations. The goal of this class is to introduce students to relevant theory and methods, in order to provide the foundational skills required to understand and critically analyze spatial epidemiologic studies. The course emphasizes spatial epidemiology as a sub-discipline of epidemiology while acknowledging the many scientific disciplines that shape it, including biostatistics, cartography, criminology, demography, economics, geography, psychology, and sociology. We begin by defining spatial epidemiology and exploring these multi-disciplinary roots, with particular regard to the theoretical causal mechanisms that provide a bridge between social and physical environmental conditions and population health. We then provide a basic overview of geographic information systems and their utility for descriptive spatial epidemiology—including data visualization and cluster detection—before demonstrating how to incorporate spatial structures within conventional epidemiologic study designs to examine associational and causal relationships between environmental conditions and health outcomes.

episummer Program Evaluation for Public Health Professionals Course Description Program evaluation is the systematic collection of data about program characteristics, outcomes and impacts to document the merit or worth of a specific program, and is a fundamental component of program planning, implementation, and improvement. The growing demand for programmatic efficiency and accountability has increased the need for public health professionals skilled in program evaluation. This course introduces the concepts and functions of program evaluation, and will provide practical experience in translating concepts into application for actual programs. Course materials, examples and exercises will draw on programs implemented in resource-rich and resource-limited settings.

The Single Cell Analysis Boot Camp is a two-day intensive training of seminars and hands-on analytical sessions to launch students on a path towards mastery of scRNASeq data analysis methods used in health studies. This two-day intensive boot camp is designed as a flipped classroom setup with several pre-recorded lectures for attendees to watch prior to each training, with live training times designed for hands-on labs and answering questions. Emphasis will be given to single cell data collection and basic analysis, along with in-depth sessions on advanced methods for phenotyping single cell populations using systems-biology approaches. Led by a team who have invented several of the methods used in network biology and single-cell transcriptome analysis, we demonstrate how to use network models to convert gene expression profiles into protein activity profiles, and how to transfer knowledge between established bulk datasets and novel single-cell data. We expect that, during this hands-on workshop, participants will acquire enough knowledge to plan and perform scRNAseq analyses. By the end of the workshop, participants will be familiar with the following topics: Gene Expression Analysis of scRNA data (pre-processing, quality control, filtering, normalization) Cluster Analysis Cell Type Identification Regulatory Network Analysis Master Regulator Analysis Audience and Requirements Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are four requirements to attend this training: Each participant must have an introductory background in statistics. Each participant must be familiar with R. Each participant must have a free, basic RStudio Cloud account prior to the first day of the workshop. Instructors Boot Camp Director: Lorenzo Tomassoni, PhD, Postdoctoral Research Scientist, Department of Systems Biology, Columbia University. Ester Calvo Fernández, PhD Candidate, Departments of Pathology and Cell Biology and Systems Biology, Columbia University. Aleksandar Obradovic, MD/PhD Candidate, Columbia University. Alessandro Vasciaveo, PhD, Associate Research Scientist, Columbia University. Luca Zanella, Staff Associate, Columbia University. Keynote Speaker: Peter Sims, PhD, Systems Biology, Columbia University ADDITIONAL INFORMATION Subscribe for updates on new Single Cell Analysis Boot Camp details or registration deadlines. Contact the Single Cell Analysis Boot Camp team. Capacity is limited. Paid registration is required to attend.