Analytics & BI
Analytics is turning data into business decisions. Not building models or engineering pipelines, but translating what the data says into something a stakeholder can act on. Dashboards, reports, KPI definitions, A/B test readouts, executive summaries. The work lives at the intersection of data, business context, and communication.
Analytics practitioners spend as much time framing the right question as answering it. The hardest part isn't the SQL or the visualization. It's understanding what the stakeholder actually needs, defining the right metric, and communicating uncertainty honestly.
The track
Projects span from basic reporting to complex experimentation and strategic analytics. You'll direct AI to query data, build dashboards, design metrics, and run experiments for fictional clients, then verify whether the numbers are right, the visualizations are honest, and the recommendations are sound.
The skill you're building isn't writing SQL or building charts. It's directing AI to do analytical work and verifying the result: knowing which metric answers the question, whether a chart is misleading, and when AI's interpretation of the data is wrong.
Before you start
- Read the Introduction: what the field is, how the work flows, what tools you'll use
- Complete the Platform Setup: accounts, terminal, Claude Code, Git (same for all tracks)
- Complete the Analytics Setup: Python, SQL tools, and a hands-on demo