data-science

Python is a sensible first language: readable syntax, a huge library ecosystem, and use in web, data, and automation work. This guide covers setup, a phased learning path, free courses, debugging, learning with AI assistants, and a project ladder.

DuckDB is an embedded analytical database — SQLite for analytics. Query Parquet, CSV, and Arrow with SQL, no ETL needed.

Master Python for data science in 2026. Covers essential libraries, workflows, career paths, and learning roadmap with real examples.

Learn NumPy from scratch — arrays, broadcasting, vectorization, indexing, and linear algebra. The bedrock library behind pandas, scikit-learn, and PyTorch.

Learn SQL from scratch — SELECT, JOINs, GROUP BY, window functions, and indexes. Practical examples for PostgreSQL, MySQL, SQLite, and BigQuery.

Learn pandas: DataFrames, loading, cleaning, groupby, merging, and plotting, with pandas 3.0 code examples and a full CSV-to-insight analysis pipeline.

Quick reference for Python data libraries 2026: Pandas, Polars, DuckDB, NumPy, Plotly. Common operations side-by-side. Bookmark for daily work.

How Apache Spark works: RDDs, lazy DAG execution, drivers and executors, Spark SQL, AQE, streaming, and when single-node tools like DuckDB or Polars fit better.

Data engineering vs data science: what each role does, the skills and tools they need, how the work differs, and how to choose or switch between them.

Data contracts explained: what they contain, how dbt model contracts and Schema Registry enforce them, where they fail, and a rollout plan for small teams.

A practical machine learning hub: the ML workflow, supervised and unsupervised learning, how models learn, metrics, common mistakes, tools, and a learning path.

dbt (data build tool) explained for data analysts: SQL-based transformations, models, tests, documentation. The modern way to build data pipelines.