During this week I came across some excellent resources. This post is an aide memoir for me but I hope it might be of interest.
Open Badge Template
This design your own open badge template appeared in the Wapisasa sandbox.
Data Science Text
Pablo Casas (2018) has published a data science live book. The introduction to Why this book?
The book will facilitate the understanding of common issues when data analysis and machine learning are done.
Building a predictive model is as difficult as one line of
my_fancy_model=randomForest(target ~ var_1 + var_2, my_complicated_data)
But, data has its dirtiness in practice. We need to sculpt it, just like an artist does, to expose its information in order to find answers (and new questions).
Michael Clark’s Documents
Michael notes of his online resource shared on GitHub:
Here you’ll find documents of varying technical degree covering things of interest to me or which I think will be interesting to those I engage with. Most are demonstration of statistical concepts or programming, and may be geared towards beginners or more advanced. I group them based on whether they are more focused on statistical concepts, programming or tools, or miscellaneous.
Michael is the Statistician Lead for the Advanced Research Computing consulting group, CSCAR, at the University of Michigan. He provides analytical, visualization, code, concept, and other support to the larger research community.
A tweet from Mara Averick led me to Sam Firke’s discussion of March Madness college basketball.
Sam shares his guide for March Madness predictions (an estimate how likely it is that Team A beats Team B, for each of the 2,278 possible matchups in the tournament).
Sam shares a link to Gregory Matthews and Michael Lopez’s (2014) paper Building an NCAA mens basketball predictive model and quantifying its success. Gregory and Michael were winners of the 2014 Kaggle competition to predict the outcome of the tournament.