Bound the question
Choose one question, such as which category has more entries. Note that minutes are sample resolution times, not a service standard.
Concrete output: One question and two assumptions.
FICTIONAL LEARNING EXERCISE
Explore fictional support requests
Answer one question using 12 records from a fictional support desk. The file is neither real customer data nor an industry benchmark.
Inspect the sample file
12 sample rows · 4 columns
| request_id | category | channel | resolution_minutes |
|---|---|---|---|
| R01 | account | 18 | |
| R02 | billing | chat | 12 |
| R03 | delivery | — | |
| R04 | account | chat | 9 |
The table shows the first four rows. The file is fictional; some exercises intentionally include missing or conflicting values. Do not interpret them as real operational outcomes.
Download fictional sample CSV ↓Inspect the data
Find the missing duration and repeated record ID. Explain your handling and keep the raw file before removing anything.
Concrete output: A short cleaning log.
Produce a small summary
Answer with one table or chart. Explain excluded rows and how you calculated totals.
Concrete output: One table/chart and explanation.
Explain the case
Write context, your decision, result and limits. Do not infer real service performance from a small fictional sample.
Concrete output: A one-page exercise case note.
My own project narrative
Write what you actually did and its limits in your own words rather than copying a prewritten achievement.
Ask yourself before saving
- Did I explain missing and repeated rows?
- Can a reader understand the table’s scope?
- Is fictional data clearly distinguished from real outcomes?