ModernDive: Statistical Inference via Data Science
Abstract: How can we help those completely new to coding, data science, data modeling, and in particular statistical inference learn these critical tools for modern data analysis? In this talk, I present a proposed resource "ModernDive - An Introduction to Statistical and Data Sciences via R" available at ModernDive.com. ModernDive is an open source electronic textbook co-authored by myself and Chester Ismay of DataCamp, built using RStudio's bookdown interface to R Markdown. Specifically I will present the book's 4 main themes: 1) introducing coding and computation to novices through tidyverse data science tools such as ggplot2 for data visualization and dplyr for data wrangling, 2) promoting multivariate thinking and data modeling with regression early on 3) using both tactile and virtual simulations to impart the theory of sampling, all building up to 4) leveraging these tools to teach statistical inference by focusing more on computation and simulation rather than mathematically-derived large sample approximations. In particular, we'll preview the new infer R package, which performs statistical inference using an expressive and intuitive syntax that follows "tidy" design principles. We’ll conclude by presenting example analyses created by undergraduate intro stats students, many of whom had little to no programming experience or statistical background.
Bio: Albert Y. Kim will be joining Smith College in Northampton MA as an Assistant Professor of Statistical and Data Sciences in July 2018. Prior to joining Smith College, he was a Decision Support Engineering Analyst in the AdWords division of Google Inc, a Visiting Assistant Professor of Statistics at Reed College, an Assistant Professor of Statistics at Middlebury College, and a Lecturer of Statistics at Amherst College.
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