Data Science Tutorials

Learn the basics of Azure Data Factory and build your first data pipeline with this step-by-step guide!

Learn to use DeepEval to create Pytest-like relevance tests, evaluate LLM outputs with the G-eval metric, and benchmark Qwen 2.5 using MMLU.

Discover how propensity scores help minimize confounding in observational research, with detailed applications in real-world studies.

Discover SQL transactions, their importance, and how to implement them for reliable database management.

Learn how to build an AI-powered code reviewer assistant using DeepSeek-V3 and Gradio.

Discover the fundamentals of Cartesian products in SQL. Learn when and how to use them effectively, and explore best practices to optimize performance.

Discover what cardinality means and how it impacts data processing, analysis, and machine learning. Learn about practical strategies and techniques for managing high cardinality, from data preprocessing to advanced optimization tools.

Discover how to leverage Databricks SQL for efficient data analytics, querying, and business intelligence with practical examples and best practices.

Learn to integrate AWS Lake Formation and AWS Glue to create secure data pipelines, configure access controls, and automate ETL workflows.

Understand how materialized views improve query performance in SQL databases. Learn the limitations and best practices of materialized views in different databases.

Explore the fundamentals of eigendecomposition and its applications in data science and machine learning.

Learn how to set up and run vLLM (Virtual Large Language Model) locally using Docker and in the cloud using Google Cloud.