Part 1 Asking questions

As an analyst, you’ll wear many hats throughout your career. Depending on the project or request, you might shift between building dashboards, auditing data for quality, or calculating a key summary statistic. The variety in our work is vast.

At the core of all these tasks lies a guiding principle: a question. Every deliverable you create, whether explicitly stated or not, is an attempt to answer a question. This framework will accompany you throughout your career—whether you’re directly answering questions (as an analyst, data scientist, or research scientist) or empowering others to do so (as a data or analytics engineer). We are, in essence, scientists—though often by another name.

This part explores how the scientific method underpins much of our work. You likely remember the steps from school: ask a question, develop a hypothesis, test it, and evaluate the results. In some cases, you’ll have the luxury of following each step meticulously; in others, you’ll be pressed to produce answers within hours. No matter the timeline, starting with a straightforward question and an open mind is crucial to setting yourself—and your stakeholders—up for success.