Data Science Tutorials

See how Amazon Rekognition can understand faces, detect objects, read text, and filter out inappropriate content. In this guide, we’ll break down how it works, its key features, pricing, and the debates around its ethical use.

Understand when and how matrices can be diagonalized, and why it matters for data science and computational linear algebra.

Learn what a dbt snapshot is, how to create one, and how to configure it to track your data changes in this tutorial.

Learn how to create a bar graph in Excel with this beginner-friendly guide. Explore chart types, formatting tips, dynamic features, and troubleshooting techniques.

Learn how to set up and use Qwen Code CLI for agentic coding tasks, including codebase exploration, refactoring, and more.

Learn what Maximum Likelihood Estimation (MLE) is, understand its mathematical foundations, see practical examples, and discover how to implement MLE in Python.

Explore KL-Divergence, one of the most common yet essential tools used in machine learning.

Discover how to use the Excel UNIQUE() function to instantly remove duplicates and extract distinct values from any list. Learn how to apply it to both rows and columns.

Dive deep into the world of p-hacking, where manipulating the p-value is tempting. Learn the different traps of p-hacking, and how to detect and prevent them.

Learn how git push force rewrites branch history, how to use it safely, and how to recover commits without breaking team workflows.

Learn the various methods of iterating over rows in Pandas DataFrame, exploring best practices, performance considerations, and everyday use cases.

Learn how to make your code modular, testable, and maintainable by understanding and implementing various Python dependency injection frameworks.