By aggregating our data in an effort to simplify it, we lose the signal and the context we need to make sense of what we’re seeing.
Data scientists and engineers don’t always play well together. We discuss an approach to your tech stack that can bring them together.
April Fool's may be over, but once we set up a system to react every time someone typed Command+C, we realized there was also an opportunity to learn about how people use our site. Here’s what we found.
Deep learning models still need testing, but many of the common testing approaches don't apply. But with the right methods, you can still make sure your pipeline produces good results.
At this point, most software engineers see the value of testing their software regularly. But are you testing your data engineering as well?
Rather than dig into complex math or over-simplify by using a pre-written function, we'll write our own binomial test function, primarily using base Python. In the process, we'll learn more about how hypothesis testing works and build intuition for how to interpret a p-value.
Investigate a dataset with summary statistics and some basic data visualizations using the Python libraries NumPy, pandas, matplotlib, and Seaborn.
In today’s tech industry, statistics and data science are becoming increasingly important and valuable skills.
The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers. As such, model deployment is as important as model building.