START HERE — APPLIED DATA ANALYSIS MODULE 02
Exploring data and finding useful patterns. Six lessons; 310 suggested minutes including exercises.
Read case_brief.txt and data_dictionary.txt. tickets.csv is copied BYTE-FOR-BYTE from Module 01; no fictional customer identities/free text. Keep original source unchanged. The current dictionary records the original source version; this module's lesson version is 2026-10-15.2.
Use a calculator/text editor or local spreadsheet. Verify UTC date parsing before subtraction. There is no supplied Excel workbook or automatic spreadsheet cleaning. Python is optional: copy worked_analysis.py.txt to a local .py file, then run with your local Python 3 and the extracted tickets.csv; standard library only. Never run exercises against hosting/customer databases.
Use learner_worksheet.txt, profile_checklist.txt, chart_plan_template.txt and evidence_note_template.txt. Compare worked_solutions.txt, expected_results.json and worked_exploration.html AFTER attempting tasks. The HTML is a static offline worked example with tables/frequency bars; it does not reload a neighbouring CSV or save work. Extract practice_pack.zip to preserve evidence links.
Bins [0,1), [1,2), [2,4), [4,8), [8,infinity) are frequency categories; they do not replace the inclusive <=4h policy. Missing ratings stay missing; six blank resolutions retain their documented meaning. No causal/p-value/business-wide claim is justified by this deliberately constructed extract.
Review with review_rubric.txt. Label guided fictional work honestly in a portfolio. Public registration, new paid enrolment and online payments remain disabled. Report corrections: support@datacareerhub.io.
