Data Science Tutorials

The t statistic helps you decide whether a difference in your data is meaningful or just random variation. This guide explains how it works, how to calculate it, and how to use it in real testing scenarios with clear, step-by-step examples.

Learn how to set up Claude Code inside Cursor, understand the difference between Claude Code and Claude models in Cursor, and decide when each workflow makes sense for your project.

A guide covering the essential proactive checks across replication, performance, and backup to keep your data platform robust and reliable.

Master OpenAI's Codex CLI to automate data workflows. Learn to conduct EDA, build Python ETL pipelines, and generate tests directly from your local terminal.

A practical overview of regularization in machine learning - what it is, how it works, and when to use L1, L2, and Elastic Net to build models that generalize.

Master the six pillars, use the AWS Well-Architected Tool, and turn reviews into a repeatable process for secure, efficient, and cost‑optimized cloud workloads.

Learn how to run PrismML's fast and efficient 1-bit Bonsai model locally on Windows, from download and setup to WebUI testing, reaching 88.6 tokens per second.

Learn how to use VLOOKUP() in Google Sheets to search, find, and pull data from large datasets in seconds.

Learn how to install and use the Claude Code VS Code extension, what it can do across your codebase, and how it compares to working from the terminal.

A deep dive into ResNet architecture, covering how residual learning and skip connections solve the vanishing gradient and degradation problems that make training deep neural networks difficult.

A practical guide to geometric series covering the finite and infinite sum formulas, convergence conditions, and real-world applications across finance, physics, and computer science.

Learn how to run GLM 5.1 locally on an H100 GPU with llama.cpp, test it, use the WebUI, and integrate Claude Code.