In this tutorial, we'll discuss 5 Automation tools for supercharging your next Data Science project
Automation has transformed many industries around the world. From self-service checkouts in supermarkets to car-building robots, technological solutions are constantly encroaching on the areas of work once the exclusive domain of humans.
As Data Scientists, we are not immune from this. Every day new products are being developed to automate parts of the Data Science life-cycle.
They say that Data Science is 80% preparation and 20% analysis and modelling. But new tools are eating into that 80%, allowing us to spend more time on the high value work at the end of the Data Science process.
Automunge makes the process of preparing tabular data for machine learning a lot quicker and easier. Taking a “tidy” dataset as input (tidy meaning one feature per column and one observation per row), it classifies each feature according to its type and transforms it as appropriate.
For example, numerical values undergo Z-score normalisation, categorical variables are one-hot encoded and missing values can be imputed using automated machine learning.
This reduces some of the time-consuming preparation work required before you’ve even started modelling. It also has the added benefit of helping standardise your approach and reducing the risk of errors.
Keeping up in the new silicon-based survival of the fittest
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