Big datasets pose computation problems for software such as R and python in addition to implementing basic machine learning algorithms that can seem like it would run forever. Most of the time it is difficult to even determine how much time it would take to run these algorithms. Enter H20,... Read more

This work is supported by ETH Zurich, Anaconda Inc, and the Berkeley Institute for Data Science At a recent Scikit-learn/Scikit-image/Dask sprint at BIDS, Fabian Pedregosa (a machine learning researcher and Scikit-learn developer) and Matthew Rocklin (Dask core developer) sat down together to develop an implementation of the incremental optimization algorithm SAGA on parallel Dask datasets. The... Read more

A new release of pyjanitor is out! Two new features that I have added in include: Concatenating column names into a single column, such that each item is separated by a delimiter. Deconcatenating a column into multiple columns, separating on the basis of a delimiter. Both of these tasks come up... Read more

In this context, outliers are data observations that are distant from other observations. There are a number of reasons why variability may exist in the data that you are working on during your analysis. Outliers may cause serious problems in your efforts as a Data Scientist. title author date Detecting Outliers... Read more

This work is supported by Anaconda Inc tl;dr: Pickle isn’t slow, it’s a protocol. Protocols are important for ecosystems. A recent Dask issue showed that using Dask with PyTorch was slow because sending PyTorch models between Dask workers took a long time (Dask GitHub issue). This turned out to be because... Read more