Gain Insights from a Comparison in Three Lines of Code — Motivation Imagine you are trying to determine if there is a significant difference in the median total payment between two cities that a taxi picks up. You decide to create a box plot to observe the total fare per pickup city. This plot gives you some ideas about…

Build a Robust Data Pipeline in Python with Deepchecks and Prefect — Motivation A data science project includes primary components such as getting data, processing data, training an ML model, then putting it into production. It is important to validate each component’s outputs to ensure each component works properly before feeding its outputs to the next component in the workflow. …

Create Complicated RegEx with Human-Readable Syntax — Motivation Imagine you are trying to find all URLs inside a text. Each of these URLs must: Start with either http:// or https:// or the domain of the URL End with either .com or .org You might end up writing a complicated regular expression(RegEx) like like the below: This RegEx is…

Get Updated with the Progress of Your Python Code with Slack Notifications — Motivation Your Python code might take hours to run. For example, you might need to train your machine learning model overnight to get the best performance. …

Get Updated with the Latest Trends on GitHub in Your Local Machine — Motivation GitHub feed is a great way for you to follow along with what’s trending in the community. You can discover some useful repositories by looking at what your connections have starred. However, there might be some repositories you don’t care about. For example, you might be only interested in Python…

Make Your Data Science Pipeline Resilient Against Failures — Motivation There are a lot of components of a typical data science pipeline such as loading data, processing data, training a model, and making predictions. As a project grows, the number of components, as well as the dependencies between them, proliferate. If each component has an independent chance of failing, it…

Automate Your ML Pipeline with GitHub Actions — Motivation Imagine your company is creating an ML-powered service. As a data scientist, you might try to continuously improve the existing ML model. Once you find a better model, how do you make sure the service doesn’t break when you deploy the new model? Wouldn’t it be nice if you can…

Gain Insights from Your URLs in Python — Motivation Have you ever tried to extract features and insights from URLs, but found it difficult to do so? Wouldn’t it be nice if you can extract features and create a nice network graph for your URLs like below?

And How to Create One in One Line of Code — Motivation It is important to structure your data science project based on a certain standard so that your teammates can easily maintain and modify your project. But what kind of standard should you follow? …