Data Science Tutorials

Remove unused containers, images, volumes, and networks safely with a single shell command. Keep your system tidy and reclaim disk space by removing unused Docker resources.

Examine the mathematical principles behind random walks and explore their forms, from one-dimensional paths to biased and Gaussian models. Use Python to discover how these stochastic processes inform real-world phenomena in biology, physics, and finance.

Explore simple subtraction for quick and effortless date calculations. Apply the DAYS() and DATEDIF() functions to achieve precise date intervals. Use NETWORKDAYS() and TODAY() to track working days and automatically exclude weekends and holidays.

It only takes a few commands from a fresh OS installation to running your first containerized application. Learn how to do this with this ultimate Docker Ubuntu setup guide.

Master how to install and use Databricks CLI for cluster management, job automation, notebook handling, and DBFS operations with tips and best practices.

Learn the hill climbing algorithm in Python. This guide covers types, limitations, and real-world AI applications with code examples.

Learn how to multiply in Excel, including how to multiply columns and rows. Learn to use simple formulas as well as functions like PRODUCT() and SUMPRODUCT().

Learn how to build and publish a mobile app using Lovable AI, integrating it with Supabase for backend services and GitHub for version control.

Learn about the new flagship vision-language model and run it on a laptop with 8GB VRAM.

Explore the differences between mean and median, learn their applications in data analysis, and know how to choose the right measure for different scenarios.

Learn to code an unbeatable Tic-tac-toe AI using the Minimax algorithm in Python. This tutorial covers theory, implementation, and optimization, ideal for game AI enthusiasts.

Learn about Python lambda functions, their purpose, and when to use them. Includes practical examples and best practices for effective implementation.