Everything else is ready: study the parts, the key terms and the quiz below.
- Part 11Start with the decision, then name the method
- Part 22Inspect the source before changing it
- Part 33Construct one count per calendar day
- Part 44Choose views in an order that answers the report question
- Part 55Record evidence and make the next check explicit
- Part 66Reproduce the report and hand off a challenge
EDA Is a Sequence of Decisions
Turn a raw table into a short list of verified observations and the next questions to test.
In this lesson6 parts
- 01Start with the decision, then name the method
- 02Inspect the source before changing it
- 03Construct one count per calendar day
- 04Choose views in an order that answers the report question
- 05Record evidence and make the next check explicit
- 06Reproduce the report and hand off a challenge
Key terms
The words this lesson introduces, each in one line. The module’s glossary collects them all.
- exploratory data analysis (EDA)
- Inspecting recorded data, its quality and its patterns before drawing conclusions.
- unit of analysis (narration)
- Whatever is counted once for the stated result; here, an included invoice on its recorded day.
- schema
- Field names and stored data types in a table, as
df.info()shows them. - missingness
- Absent recorded values in a stated field and set of rows: 135,080 lines have no
CustomerID. - duplicate record
- A row matching an earlier row on the compared fields: 5,268 exact repeats, to inspect, not delete silently.
- data-quality check
- A test for missing, repeated or invalid recorded values before a declared use. It records an issue; the decision about it comes after.
- univariate view (narration)
- A view of one variable on its own, such as the distribution of daily invoice counts.
- bivariate view (narration)
- A view of two variables together, such as each daily count against its date.
- subgroup view (narration)
- The same measure split by a declared category, such as recorded country.
- evidence log (narration)
- A record of each observation, a plausible alternative explanation and the next check.
Quiz 5 questions
Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.
Practice
Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.
The self-checking notebook for this lesson is Stats 2.1, 2.2 and 2.7 · EDA and a chart audit.
Common mistakes
What you will see when it goes wrong, why it happens, and the fix.
Where it’s used
Where this lesson’s ideas turn up in real work.