Data Science Tutorials

Understand how to derive the characteristic equation of a matrix and explore its core properties. Discover how eigenvalues and eigenvectors reveal patterns in data science applications. Build a solid foundation in linear algebra for machine learning.

Learn how to squash commits on a branch using interactive rebase, which helps maintain a clean and organized commit history.

Learn how to safely undo a Git merge using `git revert`, preserving commit history and resolving potential conflicts.

Explore everything you need to know about how to implement the bag of words model in Python.

Learn how to use OpenAI’s gpt-4o-audio-preview model with LangChain to build voice-enabled applications that include audio input, transcription, and generation.

Discover the essentials of depth-first search for navigating graphs and trees. Implement DFS in Python using recursion and iteration, and see how DFS compares to breadth-first search and Dijkstra’s algorithm.

Learn how to use a Python API to connect systems and give your projects real-time data. Discover how to retrieve, send, and process data, and make your applications dynamic and responsive.

Discover the power of the geometric mean in finance, biology, and data science. Learn how to calculate it, when to use it, and why it's useful for analyzing growth rates.

Learn how to build secure, local AI applications that protect your sensitive data using a low/no-code automation framework.

Get step-by-step instructions on how to set up and run Llama 3.2 1B on your Android device using the Torchchat framework.

Learn how to find p-value in Excel using Excel functions like T.TEST() and Z.TEST(), by using the Data Analysis Toolpak, or through manual calculations. Equip yourself to make confident, data-backed decisions in Excel.

Ready to start with dbt and take data modeling to the next level? This hands-on tutorial dives into essential techniques and project patterns to help you build intuitive, high-quality data warehouses with dbt.