Data Science Tutorials

Gain confidence in OLS regression by mastering its theoretical foundation. Explore how to perform simple implementations in Excel, R, and Python.

Remove unnecessary spaces, including leading, trailing, and extra spaces, to keep your data clear. This guide covers TRIM(), SUBSTITUTE(), and CLEAN() functions, plus practical tips to ensure spreadsheets remain accurate and easy to work with.

Choose the right Python list reversal technique between reverse(), slicing, reversed(), and list comprehensions. Use these methods to tackle tasks like data sorting and sequence alignment.

Discover how to handle complex criteria in a single formula. Elevate your reporting in a way that saves time and effort and reduces mistakes.

Learn how to use the demand curve to reveal a relationship between price and quantity sold, and use this knowledge to guide smarter decisions in competitive markets. Explore methods to find the optimal price to maximize overall revenue.

Discover how to enhance your data analysis with the Excel slicer. Learn advanced techniques to build dynamic dashboards and streamline your workflow.

Learn the basics of AWS Lambda, how to set up your first function, and how to integrate it with core AWS services for application serverless deployment.

Understand the basics of Reinforcement Learning (RL) and explore the Gymnasium software package to build and test RL algorithms using Python.

This beginner-friendly tutorial will guide you through the fundamentals of Metaflow. Learn how the framework simplifies building and scaling data science workflows.

Understand the core concepts of Self Organizing Maps and learn how to implement them in Python using MiniSom.

Learn how to use pytest-mock, from setup and fundamentals to advanced techniques for practical testing.

Learn how to build a multilingual code explanation app using Llama 3.3, Hugging Face, and Streamlit.