Predicting job performance is often a key component of an I/Os role. However, getting access to high quality data can often times be difficult. All is not lost though...we can look to other areas to get access to data that may be useful. One such example is Baseball...Bill James' of Moneyball fame produced a statistics revolution in baseball and with that has come access to more data over the years. In this post I'll look at baseball data to see if we can predict whether or not a ball put in play results in a hit or an out, similar to good job performance or bad job performance.
In this article I revisit the Glassdoor data and perform some EDA and visualizations on it including how to create maps, new features, violin plots, and much more.
Many people think they can't get into data science or machine learning because they don't have the necessary hardware. Sometimes the hardware needed for complex calculations is a GPU, which can be quite expensive. In this article I'll show you how to use Kaggle kernels to gain free access to a GPU.
Many people think they can't get into data science or machine learning because they don't have the necessary hardware. Sometimes the hardware needed for complex calculations is a GPU, which can be quite expensive. In this article I'll show you how to use Google Colab to gain free access to a GPU.
This post provides links to the resources mentioned in the 2019 Panel Discussion ""So You Want to Be a Data Scientist: A Self-Guided Curriculum"
© N. Koenig 2016
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