Journal of Data Science

Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects regression models. The central challenge is to both mitigate bias induced from potentially unbalanced samples and to incorporate adjustments for differences in effective sample size to get correct variance and interval estimates. We present a motivating example of Bayesian inference for a multi-level or mixed effects model in which estimates of both the local parameters (e.g. group level random effects) and the global parameters need to be adjusted for the complex sampling design. We evaluate the limitations of the survey-weighted pseudo-posterior and an existing automated post-processing method to improve the uncertainty quantification. We propose modifications to the automated process and demonstrate their improvements for multi-level models via a simulation study and a motivating example from the National Survey on Drug Use and Health. Reproduction examples are included in the supplementary material and the updated R package is available via github: https://github.com/RyanHornby/csSampling

The programming overhead required to implement machine learning workflows creates a barrier for many discipline-specific researchers with limited programming experience. The stressor package provides an R interface to Python’s PyCaret package, which automatically tunes and trains 14-18 machine learning (ML) models for use in accuracy comparisons. In addition to providing an R interface to PyCaret, stressor also contains functions that facilitate synthetic data generation and variants of cross-validation that allow for easy benchmarking of the ability of machine-learning models to extrapolate or compete with simpler models on simpler data forms. We show the utility of stressor on two agricultural datasets, one using classification models to predict crop suitability and another using regression models to predict crop yields. Full ML benchmarking workflows can be completed in only a few lines of code with relatively small computational cost. The results, and more importantly the workflow, provide a template for how applied researchers can quickly generate accuracy comparisons of many machine learning models with very little programming.

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.

Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics & statistics. In today’s data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment. MatLab can be used to teach introductory mathematics such as calculus and statistics. Both Python and R can be used to make decisions involving big data. On the one hand, Python is perfect for teaching introductory statistics in a data rich environment. On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.