[Skip to Content]
Visit us on Facebook Visit us on FacebookVisit us on YouTube Visit us on YouTubeVisit us on Twitter Visit us on TwitterVisit our RSS Feed View our RSS Feed
The MARquee August 11th, 2020
CategoriesCategoriesCategories Contact UsContact Us ArchivesArchives Region/OfficeRegion SearchSearch



Date prong graphic

BD2K 2017 Webinar Series: Fundamentals of Data Science

Posted by on December 20th, 2016 Posted in: Data Science, Education, Health Professionals, News from NLM/NIH

The NIH Big Data to Knowledge program is pleased to announce the spring semester of The BD2K Guide to the Fundamentals of Data Science, a series of online lectures given by experts from across the country covering a range of diverse topics in data science.  This course is an introductory overview that assumes no prior knowledge or understanding of data science.

The series will run through May, meeting once per week at 12-1pm Eastern Time/9am-10am Pacific Time. No registration is required.

For more information and to join the lecture, visit the series event page.

This is a joint effort of the BD2K Training Coordinating Center, the BD2K Centers Coordination Center, and the NIH Office of the Associate Director of Data Science.

The first semester of the series covered Data Management and Data Representation.  This semester will cover computing, data modeling, and overarching topics. You can also explore archived presentations on the series YouTube channel.

The scheduled topics for January-May of 2017 are as follows:


  • 1/6/17:   Computing Overview (Patricia Kovatch, Mount Sinai)
  • 1/13/17: Workflows/pipelines (Rommie Amaro, UCSD)
  • 1/20/17: Running a Data Science Lab (Trey Ideker, UCSD)
  • 1/27/17: Modern Computing: Cloud, Parallel, Distributed, HPC (Umit Catalyurek, GA Tech)
  • 2/3/17:   Commons: lessons learned, current state (Vivien Bonazzi, NIH)


  • 2/10/17: Data Modeling Overview (Rafael Irizarry, Harvard)
  • 2/17/17: Supervised Learning (Daniela Witten, U Washington)
  • 2/24/17: Unsupervised Learning (Ali Shojaie, U Washington)
  • 3/3/17:   Algorithms, incl. Optimization (Pavel Pevzner, UCSD)
  • 3/10/17: Bayesian inference (Mike Newton, U Wisconsin)
  • 3/17/17: Data issues: Bias, Confounding, and Missing data (Lance Waller, Emory)
  • 3/24/17: Causal inference (Joe Hogan, Brown)
  • 3/31/17: Data Visualization tools and communication (Nils Gehlenborg, Harvard)
  • 4/7/17:   Modeling Synthesis (John Harer, Duke)


  • 4/14/17: Open science (Brian Nosek, UVa)
  • 4/21/17: Data sharing (Christine Borgman and Irene Pasquetto, UCLA)
  • 4/28/17: Ethical Issues (Bartha Knoppers, McGill)
  • 5/5/17:   Reproducible Research (John Ionnaidis, Stanford)
  • 5/12/17: Additional considerations for clinical data (Zak Kohane, Harvard)
  • 5/19/17: SUMMARY and NIH context

Reasonable accommodation: Individuals with disabilities who need reasonable accommodation to participate in this event should contact Tonya Scott at 301-402-9827. Requests should be made at least 5 business days in advance of the event.

Image of the author ABOUT Hannah Sinemus
Hannah Sinemus is the Web Experience Coordinator for the Middle Atlantic Region (MAR). Although she updates the MAR web pages, blog, newsletter and social media, Hannah is not the sole author of this content. If you have questions about a MARquee or MAReport posting, please contact the Middle Atlantic Region directly at nnlmmar@pitt.edu.

Email author View all posts by
This project is funded by the National Library of Medicine, National Institutes of Health, Department of Health and Human Services, under Cooperative Agreement Number UG4LM012342 with the University of Pittsburgh, Health Sciences Library System.

NNLM and NETWORK OF THE NATIONAL LIBRARY OF MEDICINE are service marks of the US Department of Health and Human Services | Copyright | Download PDF Reader