Data Science Tutorials

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.

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.

Discover how breadth-first search systematically explores nodes and edges in graphs. Learn its level-by-level approach to ensure the shortest path in unweighted networks. Apply BFS across data science, AI, and networking fields.

Learn how to predict the winner of the 2024 US presidential election using Python, machine learning, and data from FiveThirtyEight and the Federal Election Commission.

Learn how to use Runway Act-One through both the web interface and its API, and discover how to combine it with Runway Gen3-Alpha.

Explore the different methods for filtering out and permanently removing duplicate rows using SQL. Learn the practical applications of how to remove duplicates in SQL Server, MySQL, and PostgreSQL.

Learn how to model and solve optimization problems using Pyomo, a powerful Python library. Explore practical examples from linear and nonlinear optimization!

Learn how to streamline your LLM evaluations with MLflow. This guide covers MLflow setup, logging metrics, tracking experiment versions, and comparing models to make informed decisions for optimized LLM performance!

Explore the arithmetic mean's role in data analysis. Learn its formula, applications, and how it compares to other kinds of means and other statistical measures, and understand when each is most useful.