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Webinar Series: Fundamentals in Data Science

Modified: January 13, 2017

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 12noon-1pm Eastern Time/9am-10am Pacific Time. No registration is required! 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.
To join the lecture, visit this website.
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 upcoming semester will cover computing, data modeling, and overarching topics. To see archived presentations, go to: http://www.bigdatau.org/data-science-seminars.
SCHEDULE - The following topics will be covered in January through May of 2017:

January 6:  Computing Overview (Patricia Kovatch, Mount Sinai)
January 13: Workflows/pipelines (Rommie Amaro, UCSD)
January 20: Running a Data Science Lab (Trey Ideker, UCSD)
January 27: Modern Computing: Cloud, Parallel, Distributed, HPC (Umit Catalyurek, GA Tech)
February 3:  Commons: lessons learned, current state (Vivien Bonazzi, NIH)
February 10: Data Modeling Overview (Rafael Irizarry, Harvard)
February 17: Supervised Learning (Daniela Witten, U Washington)
February 24: Unsupervised Learning (Ali Shojaie, U Washington)
March 3:  Algorithms, incl. Optimization (Pavel Pevzner, UCSD)
March 10: Bayesian inference (Mike Newton, U Wisconsin)
March 17: Data issues: Bias, Confounding, and Missing data (Lance Waller, Emory)
March 24: Causal inference (Joe Hogan, Brown)
March 31: Data Visualization tools and communication (Nils Gehlenborg, Harvard)
April 7:  Modeling Synthesis (John Harer, Duke)
April 14: Open science (Brian Nosek, UVa)
April 21: Data sharing (Christine Borgman and Irene Pasquetto, UCLA)
April 28: Ethical Issues (Bartha Knoppers, McGill)
May 5:  Reproducible Research (John Ionnaidis, Stanford)
May 12: Additional considerations for clinical data (Zak Kohane, Harvard)
May 19: SUMMARY and NIH context

Training Grant Director, Science, Technology, Engineering, and Math, Professional Development