Data Science Tutorials

Discover how to create tables in Databricks using the CREATE TABLE command. Learn different methods for different scenarios, including how to a create a table from existing data and how to CREATE TABLE as SELECT.

This guide shows you how to build a question-answering AI app, serve it locally, and deploy it on BentoCloud—all with just a few lines of Python.

Learn how to create a simplified implementation of the RStar framework using a combination of neural networks, symbolic reasoning, and Monte Carlo Tree Search (MCTS).

Learn how to set up and run the Genesis Physics Engine on Google Colab, overcoming installation challenges and getting started with physics simulations.

Learn how to keep important rows and columns visible while you scroll in Excel. I’ve explained Freeze Panes step-by-step, with tips for solving everyday problems and improving your workflow.

Learn how to build a semantic layer with dbt, turning raw data into consistent metrics for analytics and BI tools!

Discover the importance of multilevel modeling in analyzing hierarchical data structures. Learn how to account for variability within and between groups using fixed and random effects. Apply these concepts to uncover deeper insights in fields like education, healthcare, and social sciences.

Learn about Mistral's Codestral 25.01, including its capabilities, benchmarks, and how to set it up in VS Code for code assistance.

Learn how first normal form (1NF) can improve your database design by enforcing atomicity in your tables.

Learn about Python abstract classes, their purpose, and how to use the `abc` module to enforce consistent interfaces. Includes practical examples and best practices for effective implementation.

Discover how to get started with AWS MSK, a managed Kafka service, in this beginner-friendly guide packed with practical tips and a comparison of top alternatives.

Discover the importance of dimensionality reduction, its techniques, and how to apply them to image datasets while visualizing and comparing data in lower-dimensional spaces.