Records and files

Represent, validate, analyse, and exchange persistent data

Operational analysis depends on records that persist beyond one program run: orders in a CSV file, products in a table, or experimental results exchanged between Python and Excel. Reliable analysis begins by defining what each record means and checking that the data satisfy that definition.

This part moves from individual values to datasets. You will make schemas, validation rules, grouped calculations, and file boundaries explicit so that a result remains interpretable after it leaves the program that produced it.

Learning objectives

By the end of this part, you will be able to:

• represent one record with a dictionary and a dataset with a list of records;
• define a schema containing required fields, types, permitted values, units, and missing-value rules;
• read and write CSV files using explicit input and output paths;
• validate, convert, accept, or reject incoming records with informative reasons;
• calculate grouped counts, totals, averages, rates, and operational KPIs with explicit denominators;
• export a stable result table that can be inspected in Excel;
• translate a visible spreadsheet procedure into a testable Python workflow; and
• cross-check a reported result against source rows or an independent calculation.

NoteRelease status

This page previews Module 6. Canvas announces when its detailed chapters become available on the public course site.