analytics

Almost every business question reduces to one of a small number of analytical shapes. This lesson covers trend, breakdown, funnel, cohort and distribution, what each is good for, how each misleads, and what you may and may not conclude from any of them.

The handful of spreadsheet techniques that cover most business analysis, and the specific ways they fail silently. Covers pivot tables, lookups and what a join actually does, structuring data so tools can use it, and the errors that produce plausible wrong answers.

Most analysis fails at the start, on a question too vague to answer or one the data cannot address. This lesson covers turning a business question into a measurable one, what your data was actually collected for, and the checks that come before any calculation.

Marketing's usual metrics cannot answer whether AI helped, because output volume rose and attribution is already hard. This lesson covers what to measure instead, the brand and legal risks that concentrate in this function, disclosure norms with clients and audiences, and the honest reckoning on which claimed gains survive scrutiny.

Marketing adopted AI faster than almost any function and has the least reliable evidence about whether it helped. This lesson maps the capability honestly across the funnel, separates the uses where output volume is the point from those where quality is, and explains why marketing's measurement problem makes the question unusually hard to answer.

Learn the foundational concepts of Snowflake, the cloud-native data platform. Explore its unique architecture, key features like time travel and zero-copy cloning, and understand how its pricing model works.