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Fundamentals in Data Science Webinar Series: 2017 Winter/Spring Schedule Announced

Posted by Posted in: Continuing Education, Data Management


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.

To join the lecture: http://www.bigdatau.org/data-science-seminars

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.  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:

SECTION 3: COMPUTING

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)

SECTION 4: DATA MODELING AND INFERENCE

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)

SECTION 5: ADDITIONAL TOPICS

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.

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