Gen Z data scientists are well suited to tackle bias in AI. Today’s leaders and practitioners need to lay the groundwork to enable them to do so.
Do you need to ditch your outliers? Here’s how to find (and remove) outliers in your data set with IQR.
To do any data science of value we need models that accurately represent our data set. Here’s how to evaluate a model’s fit to your training data.
In this step-by-step tutorial, I’ll show you how to automate your data analysis using a real-world problem.
Python scripts can automatically create and check the quality of regressions on your data sets. So what are you waiting for?
When we’re working with large amounts of data, errors are inevitable. Here’s how to check your data for errors manually using Python.
Here’s how to write Python scripts to check your data for errors (minus the tedium of doing it yourself).
Games and gaming offer a useful analogy for real life. By closely examining the way AI plays games, we can learn some valuable lessons.
Trying and failing to decipher your own codebase? Remember: Good code is its own best documentation.
Here’s how to heat up your heat maps and make sure they stand out.
Peter Grant
