PRACTICAL WORK / CONNECTED FOUNDATIONS

Python & SQL data foundations

Collect reliable programming and relational habits, then produce a tested, auditable data report.

A concrete outcome

A deterministic report pipeline with explicit schemas, coercion, missing-value and join policies, using the supplied Python fixtures and tests.

This is a learning sequence. Your course map shows the actual prerequisites and readiness for each lesson.

Build the programming and query foundation
Use Python functions, collections, files and exceptions alongside selection, joins and grouping. The numerical ML extension is optional for this route.
Python & empirical MLNo earlier CodeRigor skill required to begin SQL & relational dataNo earlier CodeRigor skill required to begin
Define the data contract
Identify entities, keys, relationships and constraints before treating records as interchangeable rows.
Relational database design1 entry prerequisites named in the course
Clean and join deliberately
Handle missing values, dates, money, duplicate keys and join cardinality, retaining an audit of decisions.
Cleaning real-world data with Python2 entry prerequisites named in the course
Turn the pipeline into a report
Separate pure transformation from input/output and finish a reproducible report with tested behavior.
Functional Python foundations2 entry prerequisites named in the course