This two day program is ideal for basic research and translational biology scientists who work with antibodies on a regular basis. Taught by active research scientists from the NIH and [....]
This course will address the basic principles of epigenetics, the role of epigenetic mechanisms in normal development and human disease, and the development of epigenetically-effective drugs. The objective of the [....]
This two-day workshop focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™. The workshop demonstrates the use [....]
Statistical analyses are regularly required for nearly every quantitative scientific study. Although simple data analysis tools are tenable for small-scale analyses, more rigorous software can enhance analysis replicability, improve data [....]
Advanced Gene Editing with CRISPR is a 3 day live remote workshop designed for researchers with entry level experience in CRISPR/Cas9 technology seeking a more in-depth immersion in state-of-art CRISPR [....]
This four-day workshop is ideal for researchers interested in developing neural lineages from iPSCs. Team taught by active researchers from Johns Hopkins, the NIH, the FDA, and Thermo Fisher Scientific, [....]
In this “LEARN BY DOING” class, students will enhance their understanding of: 1)What program evaluation is and why it is important 2)Program evaluation framework (e.g., stakeholder participation) 3)Methods for evaluating [....]
This course is designed to allow researchers to quickly dive into R and make visualizations for their own work. No previous experience in R is required. AFTER THIS COURSE YOU [....]
In this workshop, learners will learn the basics of how to use R to wrangle data, create visualizations, and conduct exploratory analyses. The workshop will use the popular “tidyverse” suite [....]
This workshop will first cover the theory and proper applications of various neural networks (multilayer perceptrons, convolutional neural networks, long-short term memory models, autoencoders, etc.). Powerful deep learning packages, such [....]
