Data Science Tutorials

Learn multiple methods to insert superscripts in Excel for both text and numbers using formatting tools, keyboard shortcuts, equations, formulas, custom number formats, and VBA. Discover the practical use cases for superscripts in professional and technical contexts.

Learn how to use the Grok 3 API for tasks ranging from basic queries to advanced features like function calling and structured outputs.

Learn how to generate and edit images with the GPT-Image-1 API, including setup, parameter usage, and practical examples like masking and combining multiple images.

Discover how to create hyperlinks in Excel using the toolbar, HYPERLINK() function, and macros. Understand dynamic link creation, formatting tips, and workarounds for common limitations.

Learn how absolute references work in Excel formulas to keep specific cell references constant. Discover when to use absolute, relative, and mixed references.

Learn how to break down time series data into meaningful components like trend, seasonality, and residuals using additive and multiplicative models. Learn about both classical and STL methods. Explore seasonal adjustments and practice forecasting with decomposition models.

Learn quick ways to reveal hidden columns, including shortcuts, special cases like column A, and how to manage hidden columns for better spreadsheet organization.

Learn how to install, set up, and run Qwen3 locally with Ollama and build a simple Gradio-based application.

Master the fundamentals of user input handling in Python, from basic prompts to advanced validation and error handling techniques. Learn how to manage secure, multiline inputs and ensure your programs are resilient and user-friendly.

A step-by-step guide for fine-tuning the Qwen3-32B model on the medical reasoning dataset within an hour.

Discover reliable methods for converting text to numbers in Excel, including built-in features, formulas, and manual techniques. Learn how to troubleshoot tricky cases where numbers stay stored as text.

Learn how to optimize PySpark joins, reduce shuffles, handle skew, and improve performance across big data pipelines and machine learning workflows.