A curated starting point

Python & data

Learn to turn raw records into results you can explain. Start with Python values and control flow, then work through data validation, SQL, and statistical interpretation. The tutorials use small inputs so you can predict an answer before running the program.

What should I learn first?

If you are new to coding, follow the 30 Days Python Challenge. If you already write basic Python, begin with the CSV analysis capstone and choose a deeper article when your work raises a specific question. Use the browser tools to inspect a small sample before changing a whole dataset.

Inspect a sample dataset

Start with these guides

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TUTORIAL · 4 MIN READ

Your first Python program: input, variables, and output

Build a tiny calculator and learn to read what each line does.

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DATA WORKFLOW · 4 MIN READ

Read a CSV in Python without losing track of your data

Load a small file, inspect its columns, and handle the two common surprises: delimiters and missing values.

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DATA WORKFLOW · 5 MIN READ

Python data capstone: audit a CSV and write a defensible summary

Combine CSV reading, validation, grouping, and statistics in a complete small project with a reconciliation check.

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DATA WORKFLOW · 9 MIN READ

Pandas Merge Validation: Stop Duplicate Rows and Audit Missing Matches

Use Pandas merge validation to prevent duplicate rows, audit unmatched keys, handle nulls deliberately, and reconcile totals with reproducible examples.

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DATA WORKFLOW · 8 MIN READ

SQL NULL Explained: Correct Filters, Counts, Averages, and Joins

Understand SQL NULL with tested SQLite examples. Avoid NOT IN traps, misleading averages, and left join filters that silently remove unmatched records.

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STATISTICS · 5 MIN READ

Mean or median? A five-number example with an outlier

See why one unusual value can shift the average and how to choose a useful summary.

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PYTHON BASICS · 7 MIN READ

Python Package Provenance: What Attestations Prove—and What They Do Not

Learn how PyPI attestations, trusted publishing and hash checks fit together, then build a practical release-review record without confusing provenance with safety.

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TUTORIAL · 4 MIN READ

How to Structure a Python Project With pyproject.toml in 2026

A practical Python packaging guide: understand pyproject.toml, src layouts, build backends, dependencies, and a safe first package.

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DATA WORKFLOW · 4 MIN READ

DuckDB and Parquet: A Practical Local Analytics Workflow Without a Database Server

Learn how DuckDB queries Parquet files with SQL, why columnar storage matters, and how to build a reproducible local analytics workflow.

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DATA WORKFLOW · 4 MIN READ

Apache Iceberg 1.12.0: What Data Engineers Should Know Before Upgrading

Apache Iceberg 1.12.0 arrived October 5, 2026. Explore Variant, geospatial types, deletion vectors and breaking compatibility changes.

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TUTORIAL · 4 MIN READ

Free-Threaded Python 3.14: A Benchmark Worksheet You Can Reproduce

Compare serial and threaded execution with correctness checks, repeated timings, interpreter details, and an honest record of what the benchmark covers.

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DATA WORKFLOW · 5 MIN READ

Data Contracts Explained: Stop Bad Data Before It Reaches a Dashboard

Learn how data contracts, validation rules and ownership protect analytics pipelines from silent errors, with a worked example and checklist.

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Make the reading useful

Pick one guide, reproduce its example or apply its comparison to your question, and record the result. Follow the source links when a claim affects your decision. If an explanation leaves a step unclear, tell us the page and the step through the correction form.