Data Science Tutorials

Learn how to group data by multiple columns in SQL to perform advanced aggregations. Explore use cases, performance tips, and practical examples.

Learn the essential statistical concept that gives proper weight to different data points. This comprehensive guide covers everything from basic calculations to real-world applications in finance, academics, and data analysis, with hands-on examples in Excel and Python.

Master the MongoDB Query API with this comprehensive guide to CRUD operations, advanced filters, data aggregation, and performance-boosting indexing.

Learn how to build reliable AI agents with Pydantic AI in Python. Validate outputs, use tools, and stream insights with practical code examples.

Learn how blue-green deployment enables near-zero downtime, simple rollbacks, and safe production testing in modern DevOps and cloud-native workflows.

Learn how to share data in Snowflake. Understand setup, access control, billing, and best practices for secure, scalable data sharing in Snowflake.

This tutorial walks you through setting up, managing, and securing secrets, keys, and certificates using Azure Key Vault.

Learn how to remove files in Git repository, avoid common mistakes, and manage your repository cleanly even in complex or sensitive scenarios.

Learn how to use Google’s Gemini 2.5 Flash Image for professional AI image generation. This step-by-step guide covers setup, prompt engineering, editing workflows, and advanced features.

Learn how Vision Transformers (ViTs) leverage patch embeddings and self-attention to beat CNNs in modern image classification. This in-depth tutorial breaks down the ViT architecture, provides step-by-step Python code, and shows you when to choose ViTs for real-world computer-vision projects.

Discover how AWS Amplify streamlines full-stack cloud app development with real-time APIs, authentication, storage, and Git-based, code-first deployments.

Learn what multi-head attention is, how self-attention works inside transformers, and why these mechanisms are essential for powering LLMs like GPT-5 and VLMs like CLIP, all with simple examples, diagrams, and code.