Starting Cloud-Based Research on NHLBI BioData Catalyst® (BDC)

Dates:  July 13-14, 2023

Time: 11:00 am to 4:00 pm EST

Location: Virtual via Zoom

Available Seats: 60

Instructors:

• Emily Hughes, Bioinformatics Systems Analyst, BDC Powered by PIC-SURE, Harvard Medical School - LinkedIn

• Dave Roberson, Community Engagement Manager, BDC Powered by Seven Bridges, Velsera - LinkedIn

• Kat Thayer, Senior Project Manager, BDC Powered by Terra, Broad Institute - LinkedIn

Description: NHLBI BioData Catalyst® (BDC) is a cloud-based ecosystem that offers researchers data, analytical tools, applications, and workflows in secure workspaces. It is a community where researchers can find, access, share, store, and analyze heart, lung, blood, and sleep data resources. And it is one of NHLBI’s data repositories, where researchers share scientific data from NHLBI-funded research so they and others can reproduce findings and reuse data to advance science. By increasing access to NHLBI data and innovative analytic capabilities, BDC accelerates reproducible biomedical research to drive scientific advances that can help prevent, diagnose, and treat heart, lung, blood, and sleep disorders.

The workshop is intended for both researchers and faculty who may want to use NHLBI resources in their courses. It is recommended to have an eRA Commons ID, although it is not required. The session will have both lecture and interactive, hands-on demos: an overview of BDC with a focus on setting up an analysis on the ecosystem, including identifying data of interest, identifying relevant data analysis workflows, estimating cloud costs, and data access protocols. After completing the workshop, students can request $500 in free pilot credits to continue practicing analyses or to begin testing new analyses and will have access to on-demand support and additional training opportunities from BDC. 

Registration Closed

Hosted by the NDSA Central Hub - Clark Atlanta University

Title: Navigating the Data Science Landscape: Exploring the What, Why, and How of Data Science

Dates: July 17-18

Time: 9:30 am-4:30 pm EST
Available Seats: All seats have been filled at this time. 

Instructor: Yvonne Phillips, Adjunct Professor of Computer Science, Morehouse College; Data Science/Predictive Analytics Consultant, Trainer, Speaker - LinkedIn

Description:  The workshop aims to empower participants with a solid foundation in data science by introducing key concepts, terminologies, tools, and techniques. Participants will gain the knowledge and confidence to embark on their journey as data scientists. This workshop serves as a valuable stepping stone for individuals seeking to leverage data-driven insights in their professional careers and provides the necessary resources to support classroom instruction, research efforts, and practical applications of data science. 

Hosted by the NDSA Central Hub - Clark Atlanta University

Title: Demystifying Deep Learning

Dates: July 20-21

Time: 9:30 AM-4:30 PM

Location: Virtual via Zoom 

Available Seats: All seats have been filled at this time. 

Instructor: Dr. Antonio Moretti, Faculty Fellow, Computer Science Department, Barnard College - Website

Description: This two-day virtual Zoom workshop provides a comprehensive introduction to deep learning for beginners. Some exposure to Python, multivariate calculus, and linear algebra would be helpful. The first half of the workshop will focus on the theory of deep learning including forward and backpropagation, convolutional neural networks, sequence models, transformers, and deep generative models. The second half will serve as a tutorial on automatic differentiation where we use the Python libraries Tensorflow and Pytorch to train and deploy a model on the cloud. All disciplines are welcome. Participants will be able to train and deploy a deep learning model by the workshop's end.

Deep learning refers to a family of models based on artificial neural networks that have recently produced breakthroughs in our ability to analyze images, videos, sound and text. Facial recognition, voice search, medical imaging and self-driving cars are all examples of technologies that are powered by deep learning. This workshop will provide a clear and concise introduction to the theory and practice of deep learning including forward and backpropagation, convolutional neural networks, sequence models, transformers, and deep generative models. A tutorial on automatic differentiation will also be covered where we use the Python libraries Tensorflow and Pytorch to train and deploy a model on the cloud.

Topics include:
- Forward and backpropagation
- Convolutional neural networks
- Sequence models (RNNs and LSTMs)
- Transformers
- Deep generative models
- Automatic differentiation
- Tensorflow and/or Pytorch- Model Deployment

Hosted by the NDSA Central Hub - Clark Atlanta University

Title:  Social Science with R Studio/Posit

Dates: July 24-25

Time: 9:30 am-4:30 pm EST

Available Seats: All seats have been filled at this time. 

Instructor: Lyrric Jackson, Adjunct Professor of Dance, Spelman College; Certified Data Carpentries Instructor - LinkedIn

Lyrric is an American multidisciplinary artist, educator, and data scientist. She received her Bachelors in Drama/Dance Concentration from Spelman College and holds a Masters in Arts Administration and a MFA in LXFM from the Savannah College of Art and Design. She is a Professor of Dance Performance and Choregraphy at Spelman College, an AUCC Data Science Initiative Faculty Affiliate, a Spelman Faculty CODE Scholar, a Certified Data Carpentries Instructor, and a Sloan Data Science Faculty Fellow. Her work extends to local and global arts and educational institutions, including Lyrric Jackson Dance, Brenau University, Emory University, and Mashirika Performing Arts in Kigali, Rwanda. 

Description: This workshop uses a tabular interview dataset from the SAFI Teaching Database and teaches data cleaning, management, analysis and visualization. There are no prerequisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.

Hosted by the NDSA Central Hub - Clark Atlanta University