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BD2K Guide to the Fundamentals of Data Science- Beginning this Friday!

Posted by on September 7th, 2016 Posted in: Data Science, Education, Health Professionals

The NIH Big Data to Knowledge program is pleased to announce 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.

This is a joint effort of the BD2K Training Coordinating Center (TCC), the BD2K Centers Coordination Center (BD2KCCC), and the NIH Office of the Associate Director of Data Science.

When: each Friday at noon Eastern Time (9am Pacific) beginning September 9th, 2016.

Please join from your computer, tablet or smartphone: https://attendee.gotowebinar.com/register/341938597813942273 (updated 11/15/16)

You may also dial in using your phone.
United States : +1 (872) 240-3311
Access Code: 786-506-213

For up-to-date information about the series and to see archived presentations, go to: http://www.bigdatau.org/data-science-seminars.

Tentative schedule:

9/9/16:  Introduction to big data and the data lifecycle (Mark Musen, Stanford)

9/16/16:  SECTION 1: DATA MANAGEMENT OVERVIEW (Bill Hersh, Oregon Health Sciences)

9/23/16:  Finding and accessing datasets, Indexing  and Identifiers (Lucila Ohno-Machado, UCSD)

9/30/16:  Data curation and Version control (Pascale Gaudet, Swiss Institute of Bioinformatics)

10/7/16:  Ontologies (Michel Dumontier, Stanford)

10/14/16:  Metadata standards (Zachary Ives, Penn)

10/21/16:  Provenance (Suzanne Sansone, Oxford)


11/4/16:  Databases and data warehouses, Data: structures, types, integrations (Chaitan Baru, NSF)

11/11/16:  No lecture ‹ Veteran¹s Day

11/18/16:  Social networking data (TBD)

12/2/16:  Data wrangling, normalization, preprocessing (Joseph Picone, Temple)

12/9/16:  Exploratory Data Analysis (Brian Caffo, Johns Hopkins)

12/16/16:  Natural Language Processing (Noemie Elhadad, Columbia)

1/6/17:   SECTION 3: COMPUTING OVERVIEW (Dates tentative)

1/13/17:  Workflows/pipelines

1/20/17:  Programming and software engineering; API; optimization

1/27/17:  Cloud, Parallel, Distributed Computing, and HPC

2/3/17:  Commons: lessons learned, current state


2/17/17:  Smoothing, Unsupervised Learning/Clustering/Density Estimation

2/24/17:  Supervised Learning/prediction/ML, dimensionality reduction

3/3/17:  Algorithms, incl. Optimization

3/10/17:  Multiple testing, False Discovery rate

3/17/17:  Data issues: Bias, Confounding, and Missing data

3/24/17:  Causal inference

3/31/17:  Data Visualization tools and communication

4/7/17:  Modeling Synthesis


4/14/17:  Open science

4/21/17:  Data sharing (including social obstacles)

4/28/17:  Ethical Issues

5/5/17:  Extra considerations/limitations for clinical data

5/12/17:  reproducibility

5/19/17:  SUMMARY and NIH context

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.

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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.

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