Data Science Tutorials

Few-shot prompting is a technique in which an AI model is given a few examples of a task to learn from before generating a response, using those examples to improve its performance on similar tasks.

Zero-shot prompting is a technique in which an AI model is given a task or question without any prior examples or specific training on that task, relying solely on its pre-existing knowledge to generate a response.

Discover the output and performance differences between UNION and UNION ALL: UNION removes duplicates while UNION ALL includes them.

Combine VLOOKUP() with IF() for efficient data analysis, including conditional lookups, error handling, and dynamic column indexing.

Create stunning boxplots in Python! This comprehensive beginner’s tutorial covers Matplotlib and Seaborn, helping you visualize and compare data distributions.

Learn how to calculate and apply Manhattan Distance with coding examples in Python and R, and explore its use in machine learning and pathfinding.

Learn how to rank data in Excel with RANK(), RANK.EQ(), and RANK.AVG() functions. Understand their differences, applications, and tips for accurate data analysis.

To remove duplicates from a Python list while preserving order, create a dictionary from the list and then extract its keys as a new list: list(dict.fromkeys(my_list)).

Learn how to convert technical models into interactive user interfaces with Gradio in Python.

Read our step-by-step instructions for how to upgrade Pip and Python on Windows, macOS, and Linux. Keep your environment up-to-date and compatible.

Learn the differences between RAG and Fine-Tuning techniques for customizing model performance and reducing hallucinations in LLMs.

Learn the fundamentals of data version control in DVC and how to use it for large datasets alongside Git to manage data science and machine learning projects.