Our Data Science Foundations online course is the perfect way to launch a new career in data science, or supercharge a current career with new data science skills. It’s also the best way to prepare for our highly selective Fellowship program. Russell Martin, after completing Data Science Foundations, also completed our Applied Machine Learning online course and then joined the Data Science Fellowship for Summer 2018 in Washington, DC. Russell’s next step will be joining The Data Incubator team as a Data Scientist in Residence at our Washington, DC location.

Longitudinal Targeted Maximum Likelihood Estimation (ltmle)Targeted Maximum Likelihood Estimation (TMLE) of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitu …

In case you missed them, here are some articles from August of particular interest to R users.

Democratizing Deep Learning – The Stanford Dawn Project

Someone who wishes to remain anonymous points to a new study of David Yeager et al. on educational mindset interventions (link from Alex Tabarrok) and asks:

We want to build agents that can accomplish arbitrary goals in unstructured complex environments, such as a personal robot that can perform household chores. A promising approach is to use deep reinforcement learning, which is a powerful framework for teaching agents to maximize a reward function. However, the typical reinforcement learning paradigm involves training an agent to solve an individual task with a manually designed reward. For example, you might train a robot to set a dinner table by designing a reward function based on the distance between each plate or utensil and its goal location. This setup requires a person to design the reward function for each task, as well as extra systems like object detectors, which can be expensive and brittle. Moreover, if we want machines that can perform a large repertoire of chores, we would have to repeat this RL training procedure on each new task.

Statistical Analyses, Mixed Models, and Second Dates: Enjoy these new, intriguing, and overlooked datasets and kernels.

Datasets are scattered across the web, tucked into cobwebbed corners where nobody can find them. Google Dataset Search aims to make the process easier:

Heterogeneous Group Square-Root Lasso (HGSL)Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see …