Writing up findings

Why it matters

An analysis only creates value when someone reads it and acts. Most decision-makers will read one page, often just the first paragraph. Writing a clear findings summary is therefore as important as the code behind it, and it is what hiring managers look for in a portfolio project.

The one-page structure

1. Headline (one or two sentences). The answer to the main question, with the key number. Write it so that someone who reads nothing else still gets the point.

Median delivery time rose from 31 to 37 minutes between August and September. Almost all of the increase comes from evening orders in Leeds and York.

2. Context (two or three lines). The question, the data (source, period, rows) and the definition of the main measure.

3. Key findings (three to five bullets). Each bullet is one claim, backed by a number and, where useful, one chart.

• Evening (5 to 8 pm) orders in Leeds and York had a median of 46 minutes in September, up from 33 in August.
• Those orders are 22% of September volume.
• Other cities and times were unchanged (within 1 minute).

4. Recommendation or next step. What the reader could do, framed by the evidence: "Consider adding evening drivers in Leeds and York; a two-week trial would show whether delivery times recover."

5. Caveats and method (short). What could make the findings wrong: missing data, a short period, no causal design, cleaning decisions (for example "1 duplicate row removed; 2% of orders lacked a delivery time and were excluded"). Link to the notebook for detail.

Writing well about numbers

• Lead with the answer, not with how you got it. Method goes last.
• Round sensibly. "37 minutes", not "36.8421 minutes"; "22%", not "21.96%".
• Give comparisons. A number alone means little: "37 minutes, up from 31".
• Use the reader's language. "Evening orders" rather than hour_bucket == 3.
• Say what is uncertain. "Based on four weeks of data" or "the difference is small relative to week-to-week variation".
• Avoid causal words (caused, drove, because of) unless your design supports them; prefer "coincided with", "is associated with".
• One chart per key finding at most, each with a title that states the finding (from module 2).

Turning notebook output into findings

A useful habit: under each analysis cell in your notebook, write one markdown sentence starting "This shows...". At the end, those sentences are your draft bullets. Then sort them by importance and cut any that do not help the reader decide.

Reproducibility

Before you send the summary:

• Restart the kernel and run all; check the numbers in the text match the output.
• Put the data source, the date it was pulled and the notebook link in the method section.
• If you are sharing a portfolio project, include a short README with how to run it, and never publish private or personal data.

A worked example of bad vs good

Bad: "I loaded the data and cleaned it, then did a groupby on city and hour. The mean for Leeds hour 18 was 47.3. There were some outliers. Charts attached."

Good: "Evening deliveries in Leeds and York are the main reason delivery times rose in September (median 46 minutes vs 33 in August; 22% of orders). Elsewhere times were stable. Adding evening drivers in those two cities is the most targeted fix. Caveat: one month of data; weather was not checked."

The good version answers the question, quantifies it, says what to do and admits the limits, in four sentences.

Common mistakes

• Narrating the process instead of stating findings.
• Too many numbers. Keep only those that support a decision.
• Hiding caveats, or burying the headline under them.
• Mismatched numbers between text and charts after a late change to the notebook.
• Overclaiming causes from observational data.

Quick recap

• Headline answer first, then context, 3 to 5 findings with numbers, a recommendation and short caveats.
• Round, compare, use plain language and avoid unsupported causal claims.
• Draft bullets from "This shows..." notes in your notebook.
• Rerun everything before sending, and record source and method.

Try it

The Do tab walks you through writing a one-page findings summary for an analysis you have done in this course or on a public dataset.