Cimentar Futuro Foundation

Learning Objectives

Translate a vague business question into a measurable one
Distinguish correlation from causation
Identify the right data source for a given question

Good analysis starts before you touch any data: it starts with turning a vague question like "why did sales drop?" into a measurable one like "did average order value or number of transactions fall in Lagos stores in Q3 versus Q2?" This module covers the analyst's core workflow: define the question, identify what data would answer it, and state your assumptions up front. You will also learn the difference between correlation (two things moving together) and causation (one thing actually causing the other), the most common reasoning error in business reporting, using the classic example of ice-cream sales and drowning incidents both rising in summer because of a third factor, heat, not because one causes the other.

Module Quiz

1. Before opening any dataset, the analyst's most important first step is to:

Clearly define the question the analysis should answerDelete every row with a missing valueBuild a chart immediatelyExport the data to a spreadsheet

2. A report shows that in months with more umbrella sales, raincoat sales also rise. What does this best illustrate?

Raincoat sales cause umbrella salesUmbrella sales cause raincoat salesThe data must be incorrectCorrelation, likely driven by a third factor such as rainy weather

3. Which of these is a well-framed, measurable analytical question?

Is performance bad?Did weekly active users in the 18-24 age group fall between January and March?What do customers think?Why is the business struggling?

Module 1: Thinking Like a Data Analyst