2.5 - Workflow & Documentation Practices

Question 1

Why are workflow and documentation practices important in data analysis?

Answer 1

They ensure your work is organized, reproducible, and understandable to others.

Question 2

What does a good data workflow involve?

Answer 2

A clear sequence of steps for collecting, cleaning, analyzing, and sharing data.

Question 3

Name two benefits of documenting your analysis.

Answer 3

Reproducibility and clarity for collaboration

Question 4

Writing clear explanations of your steps, formulas, or code is part of ______.

Answer 4

Documentation

Question 5

What is version control in data workflows?

Answer 5

Keeping track of changes in data, code, or reports to manage updates and avoid errors.

Question 6

Organizing files, naming consistently, and keeping backups is part of ______.

Answer 6

Workflow management

Question 7

Which of these is a documentation best practice?
A) Leaving formulas unexplained
B) Using consistent naming conventions
C) Storing files randomly
D) Avoiding comments in code

Answer 7

B) Using consistent naming conventions

Question 8

Why is reproducibility important in analytics?

Answer 8

So that others (or you in the future) can replicate results and verify findings.

Question 9

Keeping a ______ ensures you know what data you used, what transformations were applied, and what insights were found.

Answer 9

Data log / analysis record

Question 10

How does documenting assumptions help in analysis?

Answer 10

It makes reasoning transparent and allows others to understand or challenge conclusions.

Question 11

Name one tool or practice that supports workflow and documentation.

Answer 11

Examples: Google Docs, README files, comments in code, structured folders, version control (Git).