Why Do We Analyze Data?#

Stage 1 · 📋 Foundations · Lesson 01 of 56 · beginner

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Important

✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.

From data to decisions#

Data on its own is inert — rows and numbers. Analysis is what turns it into something useful: patterns you can see, questions you can answer, and, ultimately, decisions you can act on. Every technique in this course exists to move data one step further along that chain, from raw records toward a choice someone has to make.

Four kinds of question#

It helps to name what you are asking. Analysts group questions into four types, each harder and more valuable than the last:

Descriptive — what happened? (how have sales trended this year);
Diagnostic — why did it happen? (why did churn rise last quarter);
Predictive — what is likely to happen? (which customers will leave next);
Prescriptive — what should we do? (which customer to call to keep them).

Choosing the right one#

The right type is set by the business problem, not by the fanciest tool available. A dashboard answering “what happened” may be all a question needs; a churn-prevention campaign needs a predictive model and, ideally, a prescriptive recommendation on top. Much of this course is about matching the question to a method — and then, in the final stage, checking the answer is trustworthy before anyone relies on it.

Hint