APPLIED DATA ANALYSIS MODULE 02 — ATTEMPT BEFORE SOLUTIONS

LESSON 1: Profile the extract before looking for patterns
 Use  profile_checklist.txt  and the unchanged tickets.csv. Record row/key counts, cohort counts, category values and missing counts for responses, resolutions and ratings. Write six checks with a pass condition and an action for failure.   Explain why six missing resolution timestamps do not justify dropping six tickets.  Distinguish a blank rating from an observed zero-duration response.  State what the prepared source does not prove about completeness of the real business.   Retain the original file and label your exploration notes separately. 
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS:


LESSON 2: Understand distributions instead of relying on one average
 Derive or inspect all response durations, retaining ticket IDs. Calculate sum, mean, median, minimum and maximum for each cohort and for the combined extract. Show the two central sorted values used for each even-sized median.   Convert 0.25 and 3.3125 hours into minutes.  Explain why a lower mean and an unchanged maximum can both be true.  Write two sentences comparing the cohorts without claiming every ticket improved.  State what evidence would be needed before removing an eight-hour record.  
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS:


LESSON 3: Choose charts that reveal rather than manufacture patterns
 Complete  chart_plan_template.txt  for a duration distribution, a timeliness comparison and a channel-mix view. Use the shared bins to tally each cohort and reconcile to twelve.   Explain where exactly 4h and 8h fall.  Recover the inclusive timeliness counts without changing the bins.  Replace “New routing wins” with an evidence-based title.  State how unequal bin widths and colour-only distinctions can mislead.   Sketch or build one chart locally and include the underlying count table. 
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS:


LESSON 4: Compare groups and separate composition from performance
 Build a cohort-by-channel table with eligible count, timely count, rate and ticket share. Reconcile both the counts and weighted rates. Then calculate both periods using Week 1 weights and again using Week 2 weights.   Explain why 75% is the wrong unweighted overall answer.  Write one sentence about the observed result and one about the standardized result.  Inspect high-priority groups; quantify the effect of one ticket.  State why queue/priority cannot identify routing treatment.  
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS:


LESSON 5: Investigate missingness and alternative explanations
 Calculate rating coverage and respondent means by cohort and channel. Identify the group with no ratings and state its correct display. Draft three rows for an explanation ledger: an observed pattern, a plausible hypothesis and the evidence request that would test it.   Explain why the same 4.0 mean does not establish equal overall satisfaction.  Describe the harm from deleting unrated tickets before response analysis.  Distinguish no arrivals in the training file from complete business-day demand evidence.  State why two weekly values cannot establish seasonality.  
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS:


LESSON 6: Turn explored patterns into an evidence note
 Complete  evidence_note_template.txt  with a 160–220 word stakeholder note. Include source scope, one main result, the channel-mix challenge, one guardrail, specific limitations and a proposed next step.   Attach a source/profile note, duration summary and reconciled group table.  Use  review_rubric.txt  and correct any zero score.  Calculate the equal-channel-weight extension and explain it independently.  List one observation, one hypothesis and one unsupported claim from your draft.  
YOUR CALCULATIONS / EVIDENCE / LIMITATIONS: