Pulling data with SQL is only half of the job. The other half is knowing what the numbers actually tell you. Statistics gives analysts a reliable way to summarise data, to tell a real pattern from random noise, and to avoid drawing conclusions the data doesn't support.
You'll start with descriptive statistics: the mean, median, mode, and standard deviation, and when each one gives a misleading picture. Next comes hypothesis testing and p-values, the basis of A/B tests and "is this difference real?" questions. Finally you'll learn why correlation is not causation, one of the most common mistakes in business reporting.
With these ideas in place you're ready for the next module, Data Cleaning & Preparation, where you'll get raw data into shape before analysing it.
How to summarise a dataset with measures of centre and spread, and how to choose between the mean and the median.
A fast overview of the null hypothesis, what a p-value means, and when to use a t-test, chi-squared test, or ANOVA.
Why two things moving together doesn't mean one causes the other, with real-world examples of misleading patterns.