Data Science Tutorials

Explore cosine distance and cosine similarity. Discover calculations, applications, and comparisons with other metrics. Learn to implement in R and Python using numpy.

Enhance your SQL skills with the SQL PIVOT operator. Learn to convert rows into columns to create pivot tables in SQL Server and Oracle.

Discover the power of the SQL SUM() function for data aggregation. Learn how to implement rolling sums, cumulative sums, and sum multiple columns effectively.

Learn Stochastic Gradient Descent, an essential optimization technique for machine learning, with this comprehensive Python guide. Perfect for beginners and experts.

Discover three straightforward techniques to round up numbers in Python: using math.ceil() from the math module, using the decimal module, and NumPy.

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.

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.

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.