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.
List comprehensions dramatically reduce the length and complexity of your code. Here’s how to construct them.
If you thought list comprehensions improved your code, wait until you see this....
As a data scientist, developing great models and extrapolating nuanced insights won’t get you far if you can’t communicate your findings clearly. Here’s how to present your work using bokeh.
Python scripts can automatically create and check the quality of regressions on your data sets. So what are you waiting for?
Lambda functions are an extremely common form of code organization, simplification and clarification in Python. Here’s how to write them — fast.
Bias and variance are key concepts in data science and model development. Here’s what they mean and some tips on how to improve your model.
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).
