This training summarizes the python training and focuses on how to become a more reproducible science with data and analysis recommendations from start to publication.
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Becoming a Reproducible Scientist
Data Science
Intermediate Deep Learning with Keras in R and Python including how to use Tensorflow and Keras for sequential models, functional API use and model subclassing.
Data Science
Generating graphics in R - using ggplo2 from beginning with graphics through saving plots, themes, and facets.
Data Science
Intro to Machine Leaning with PythonAn introduction toe machine learning with the python package scikit-learn covers many supervised and unsupervised learning techniques.
Data Science
Generating data and manipulating objects will introduce data object, structure, some useful functions to describe data, and data manipulation. (Part II - includes dplyr)
Data Science
Reproducible Workflows in R utilizes the targets package to create "make"-like workflows
Data Science
Reproducible Workflows in R on HPC utilizes the targets package to create "make"-like workflows and applying it to the HPC
Data Science
Reproducible Science with Python and Jupyter Notebooks is a short summary of the Carpentries "Reporducible Science with Jupyter Notebook" multi-day training