Skip to course content
Free course

Data Analysis and Visualization with Python / Module 9

Module 9 check

Module 9 Assessment: EDA Check

Assessment ID: DAV-M09-QA01 Estimated active time: 30-40 minutes

Part A: Short answers

  1. What is EDA for?
  2. Why should EDA start with a question?
  3. Why can an average be misleading?
  4. Why should missingness patterns be inspected?
  5. Why should correlation not be described as causation?

Part B: Finding repair

Weak finding:

> More practice causes learners to complete the course.

Rewrite it as a careful EDA observation with evidence, interpretation, and limitation.

Rubric

LevelEvidence
PassEDA includes quality checks, distributions, relationships, segment comparison, and careful findings.
ReviseEDA has useful checks but findings overstate what the data proves.
Not yetEDA is mostly random outputs without question, evidence, or limits.