Data Science Tutorials

One-hot encoding is a technique used to convert categorical data into a binary format where each category is represented by a separate column with a 1 indicating its presence and 0s for all other categories.

Learn how K-Fold Cross-Validation works and its advantages and disadvantages. Discover how to implement K-Fold Cross-Validation in Python with scikit-learn.

Integrity constraints in SQL are rules enforced on database tables to maintain data accuracy, consistency, and validity, such as ensuring unique primary keys and valid foreign key relationships.

In Python, use the .encode() method on a string to convert it into bytes, optionally specifying the desired encoding (UTF-8 by default).

Learn to use decorators like @functools.lru_cache or @functools.cache to cache functions in Python.

Boost your productivity and make debugging a breeze. Find the right Integrated Development Environment for your needs.

This snscrape tutorial equips you to install, use, and troubleshoot snscrape. You'll learn to scrape Tweets, Facebook posts, Instagram hashtags, or Subreddits.

This tutorial dives into the Databricks approach to AI & Machine Learning in the Databricks Lakehouse and introduces its latest features.

Dive into the essentials of backpropagation in neural networks with a hands-on guide to training and evaluating a model for an image classification use scenario.

Learn to master DynamoDB with Node.js in this beginner's guide. Explore table creation, CRUD operations, and scalability in AWS's NoSQL database.

Explore the basics of Shiny for Python so you can start making interactive dashboards and web applications with this new library quickly!

A comprehensive visual guide into skewness/kurtosis and how they effect distributions and ultimately, your data science project.