Bayesian Statistics in R / Full-stack data science in R

CopenhagenR - useR Group
CopenhagenR - useR Group
Public group

University of Copenhagen, CSS, room 1.1.18

Øster Farimagsgade 5B · Copenhagen

How to find us

Across from the Botanical Gardens, enter any of the two gates, go up a flight of stairs to the 1st floor and follow the signs to room 1.1.18 (building 1, 1st floor, room 18)

Location image of event venue

Details

Two exciting talks:

Bayesian Statistics in R
==========================================

by Jonas Lindeløv, Assistant Professor in Cognitive Neuroscience and Neuropsychology, Aalborg University

This workshop will give a conceptual and practical introduction to Bayesian statistics in R. Bayesian statistics have a long been known to provide a larger flexibility than other approaches but it is only in recent years that it has become easy to apply this flexibility in practice. In this talk I will discuss Bayes Factors for model comparisons (as an alternative to p values) and Utility Theory as an approach for decision making. The presentation will be based notebooks referenced below but does not require that these have been studied before the talk.

https://lindeloev.github.io/utility-theory/
https://rpubs.com/lindeloev/bayes_factors

R at scale on the Google Cloud Platform
======================================

by Michał Burdukiewicz: bioinformatician affiliated with Warsaw University of Technology, founder of the Why R? Foundation and Wrocław R Users Group (STWUR), CEO of .prot.

Data science requires more than just sufficient statistical knowledge to create a model. Data, often obtained from different sources, must be purified, combined and unified, dry analysis results visualized and the model itself made available in a form accessible to the client. The R environment provides tools to support every stage of this process: from data collection through model development to the development of web applications. During my talk, I will present package necessary the full stack, large-scale data science projects in R: drake, mlr and shinyproxy.