The NIH Big Data to Knowledge (BD2K) 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 series of high-level didactic overviews across the range of topics important for data science, intended to provide a general biomedical audience with an appreciation of the elemental issues related to data science research and applications.
The series will be held each Friday at 12 PM ET (9 AM PT) beginning September 9th, 2016. Please join from your computer, tablet or smartphone.
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.
Registration is not required. Bookmark the webinar link for easy access to our weekly event!
09/09/16 – Introduction to Big Data and the Data Lifecycle (Mark Musen, Stanford)
09/16/16 – SECTION 1: DATA MANAGEMENT OVERVIEW (Bill Hersh, Oregon Health Sciences)
- 09/23/16 – Finding and accessing datasets, Indexing and Identifiers (Lucila Ohno-Machado, UCSD)
- 09/30/16 – Data curation and Version control (Pascale Gaudet, Swiss Institute of Bioinformatics)
- 10/07/16 – 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)
- 11/04/16 – Databases and data warehouses, Data: structures, types, integrations (Chaitan Baru, NSF)
- 11/11/16 – No Lecture – Veteran’s Day
- 11/18/16 – Social networking data (TBD)
- 12/02/16 – Data wrangling, normalization, preprocessing (Joseph Picone, Temple)
- 12/09/16 – Exploratory Data Analysis (Brian Caffo, Johns Hopkins)
- 12/16/16 – Natural Language Processing (Noemie Elhadad, Columbia)
01/06/17 – SECTION 3: COMPUTING OVERVIEW (Dates Tentative)
- 01/13/17 – Workflows/pipelines
- 01/20/17 – Programming and software engineering; API; optimization
- 01/27/17 – Cloud, Parallel, Distributed Computing, and HPC
- 02/03/17 – Commons: lessons learned, current state
02/10/17 – SECTION 4: DATA MODELING AND INFERENCE OVERVIEW (Dates tentative)
- 02/17/17 – Smoothing, Unsupervised Learning/Clustering/Density Estimation
- 02/24/17 – Supervised Learning/prediction/ML, dimensionality reduction
- 03/03/17 – Algorithms, incl. Optimization
- 03/10/17 – Multiple testing, False Discovery rate
- 03/17/17 – Data issues: Bias, Confounding, and Missing data
- 03/24/17 – Causal inference
- 03/31/17 – Data Visualization tools and communication
- 04/07/17 – Modeling Synthesis
SECTION 5: ADDITIONAL TOPICS
- 04/14/17 – Open science
- 04/21/17 – Data sharing (including social obstacles)
- 04/28/17 – Ethical Issues
- 05/05/17 – Extra considerations/limitations for clinical data
- 05/12/17 – Reproducibility
- 05/19/17 – 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 (email@example.com).
Videos will be archived on YouTube at: https://www.youtube.com/channel/UCKIDQOa0JcUd3K9C1TS7FLQ.
We hope that you will enjoy this exciting and informative series of lectures on data science. Again, please instruct your students, staff, and colleagues to tune in. Share this announcement with others, too. We look forward to having you!