What you need to know
Build practical Python and SQL skills with an original record-cleaning example, a runnable SQL query, three explained exercises and a mini-project with clear completion checks.
Reliable AI work begins before a model call. You need to read records, distinguish missing information from observed values, preserve identity and explain why a row was accepted or rejected. Python is useful for explicit validation and orchestration; SQL is useful for inspecting and aggregating data at a defined grain. Neither skill is demonstrated by silently cleaning away inconvenient records.
In this guide you will build a small, original job-record fixture, normalize it without inventing fields and answer a question using SQL. The examples run locally without a model, credentials or paid services. They are deliberately small educational examples, not a production ingestion service or a claim about employer hiring demand.
Before you start
You should be able to run Python 3, recognize strings, integers, lists and dictionaries, and understand that a database table contains rows with a defined meaning. The runnable examples use only Python's standard library and a SQLite-compatible SQL query. Google's Python and GoogleSQL references provide background reading; the SQL here is not a BigQuery setup tutorial.
Use fictional records while learning. Do not copy private resumes, access tokens or confidential employer data into a public repository. Keep your original inputs separate from cleaned outputs so you can investigate a mistake without reconstructing what the source might have said.
Learning sequence
- Define one record's identity and field meanings. Decide whether a missing city is allowed and whether experience is an integer or a range.
- Write pure transformation functions: explicit input, explicit output and no hidden network or database changes.
- Add rejection reasons and test normal, missing, malformed and repeated inputs.
- Learn SELECT, WHERE, GROUP BY, COUNT and joins using a tiny dataset whose correct answer you can calculate by hand.
- Combine the pieces only after each step is independently explainable. Add durable writes and concurrency controls later, rather than confusing an in-memory demonstration with safe production ingestion.
The milestone is not finishing a tutorial. It is being able to explain where every output value came from, what happened to every rejected record and which assumptions would change when the source changes.
Worked example
The policy below accepts an exact integer experience value of zero or greater, preserves a missing city as unknown and identifies duplicates by a stable source ID. It rejects duplicate rows rather than deciding which revision wins. That is a teaching policy, not a universal deduplication rule. A real update feed needs version and correction semantics.