Difference between revisions of "SOCR News AA ASA March 2019"
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===Logistics=== | ===Logistics=== | ||
| − | * '''Website''': | + | * '''Website''': http://wiki.socr.umich.edu/index.php/SOCR_News_AA_ASA_March_2019 |
| − | * '''Speaker''': Ivo D. Dinov | + | * '''Speaker''': [http://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] |
| − | * '''Affiliation''': Statistics Online Computational Resource, Health Behavior and Biological Sciences, Computational Medicine and Bioinformatics, Michigan Institute for Data Science, University of Michigan | + | * '''Affiliation''': [http://www.socr.umich.edu/index.html Statistics Online Computational Resource], Health Behavior and Biological Sciences, Computational Medicine and Bioinformatics, [http://midas.umich.edu/ Michigan Institute for Data Science], University of Michigan |
| + | * '''Place''': [https://sph.umich.edu/ TBD (SPH/UMich)] | ||
| + | * '''Time''': 6:30-8:00 PM | ||
| + | * '''Organizer''': [https://www.meetup.com/Ann-Arbor-Chapter-of-the-American-Statistical-Association/ Ann Arbor Chapter of the American Statistical Association] | ||
===Abstract=== | ===Abstract=== | ||
New technologies and rapid advances in data science lead to effective information digitalizalization and quantization of all aspects of human experiences. The wave of complex, multisource, multiscale, heterogeneous, incomplete, and time-varying data presents difficult challenges, as well as offers unique opportunities, to deeply explore a wide gamut of natural processes. Using specific biomedical and healthcare case-studies, we will demonstrate approaches for data wrangling, aggregation, secure sharing, model-based and model-free inference, and predictive analytics. More information, slides, code, datasets, publications, learning modules, and instructional materials are available on the website of the [http://SOCR.umich.edu Statistics Online Computational Resource]. | New technologies and rapid advances in data science lead to effective information digitalizalization and quantization of all aspects of human experiences. The wave of complex, multisource, multiscale, heterogeneous, incomplete, and time-varying data presents difficult challenges, as well as offers unique opportunities, to deeply explore a wide gamut of natural processes. Using specific biomedical and healthcare case-studies, we will demonstrate approaches for data wrangling, aggregation, secure sharing, model-based and model-free inference, and predictive analytics. More information, slides, code, datasets, publications, learning modules, and instructional materials are available on the website of the [http://SOCR.umich.edu Statistics Online Computational Resource]. | ||
| + | |||
| + | ===[http://socr.umich.edu/docs/uploads/2019/Dinov_PredictiveBigDataAnalytics_AA_ASA_March_2019.pdf Slides (PDF)]=== | ||
Latest revision as of 14:02, 23 March 2019
Contents
SOCR News & Events: Ann Arbor Chapter of the American Statistical Association (ASA) Presentation
Challenges and Opportunities in Predictive Big Data Analytics
Logistics
- Website: http://wiki.socr.umich.edu/index.php/SOCR_News_AA_ASA_March_2019
- Speaker: Ivo D. Dinov
- Affiliation: Statistics Online Computational Resource, Health Behavior and Biological Sciences, Computational Medicine and Bioinformatics, Michigan Institute for Data Science, University of Michigan
- Place: TBD (SPH/UMich)
- Time: 6:30-8:00 PM
- Organizer: Ann Arbor Chapter of the American Statistical Association
Abstract
New technologies and rapid advances in data science lead to effective information digitalizalization and quantization of all aspects of human experiences. The wave of complex, multisource, multiscale, heterogeneous, incomplete, and time-varying data presents difficult challenges, as well as offers unique opportunities, to deeply explore a wide gamut of natural processes. Using specific biomedical and healthcare case-studies, we will demonstrate approaches for data wrangling, aggregation, secure sharing, model-based and model-free inference, and predictive analytics. More information, slides, code, datasets, publications, learning modules, and instructional materials are available on the website of the Statistics Online Computational Resource.
Slides (PDF)
- SOCR Home page: http://www.socr.umich.edu
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