The 2018 American Statistical Association Conference on Statistical Practice aims to bring together hundreds of statistical practitioners and data scientists—including data analysts, researchers, and scientists—who engage in the application of statistics to solve real-world problems on a daily basis. The goal of the conference is to provide participants with opportunities to learn new statistical methodologies and best practices in statistical analysis, design, consulting, and programming.
Come over and meet our team at the CSP 2018 at our exhibit table located in the Salon F of the Marriott Portland Downtown Waterfront. This is a great opportunity to learn more about our programs and to engage with us.
NISS Affiliates Lunch Meeting – Friday, February 16, 2018, from 12:30-2:00 p.m.
We cordially invite NISS Affiliates to join us for a lunch meeting on February 16, 2018, from 12:30-2:00 pm, at the Portland Marriott Downtown Waterfront in the Portland room located on Lower Level 1, 1401 SW Naito Parkway, Portland, OR. There will be guest speakers at the lunch meeting speaking on "Statistical aspects of Data Science and Big Data." This is a great opportunity to bring in your friends and colleagues who would be interested in becoming a NISS Affiliate. This lunch meeting is free for NISS Affiliates. Please CLICK HERE TO REGISTER.
NISS Half-Day Short Course
David Banks, SAMSI Director and Professor of the Practice of Statistics at Duke University, will be teaching a half-day short course on "A Survey of Modern Data Science."
Modern data science is driven by applications, and these often entail Big Data and machine learning perspectives. This short course reviews key ideas and methods in nonparametric regression - starting with cross-validation and light bootstrap asymptotics, then moving on to the additive model, the generalized additive model, and neural networks. It also covers variable selection, with the Lasso and the Median Model, and describes the p >> n problem in the context of contributions by Candes and Tao, Donoho and Tanner, and Wainwright.
Objectives of the course
The course intends to convey the intuition and heuristics that underlay the evolution of data mining, machine learning, and data science from the 1990s to the present day. The target audience
is MS-level practitioners who have some comfort with regression analysis.