The BD2K Guide to the Fundamentals of Data Science, a series of online lectures

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.

The series starts Friday, September 9th and will run all year once per
week at 12noon-1pm ET.


  *** To join the meeting online:
https://global.gotomeeting.com/join/786506213
  *** To join by phone only: +1 (872) 240-3311; Access Code: 786-506-213
  *** First GoToMeeting? Try a test session:
http://help.citrix.com/getready


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

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)

10/28/16        SECTION 2: DATA REPRESENTATION OVERVIEW  (Anita Bandrowski, UCSD)
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

2/10/17         SECTION 4: DATA MODELING AND INFERENCE OVERVIEW (Dates tentative)
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

                SECTION 5: ADDITIONAL TOPICS
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


Reasonable accommodation: Individuals with disabilities who need
reasonable accommodation to participate in this event should contact
Kristan Brown or Sonyka Ngosso at 301-402-9827. Requests should be made at
least 5 business days in advance of the event.  For questions, contact
Crystal Stewart (crystal.stewart@loni.usc.edu).


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