Knowing how to select data in SQL isn't enough. You need to know how to join tables. To join tables correctly you need to understand: - Primary keys - Foreign keys - Unique keys Here's my super-simple breakdown of each one:

3. Unique Key A unique key is similar to a primary key, but it doesn't have to be the main identifier for a table. It's a column (or columns) that contains unique values, like a primary key. However, a table can have multiple unique keys, while it can only have one primary key. Unique keys are handy when you want to make sure that certain columns have distinct values, but they don't serve as the primary way to identify each row. For example, in a customer table, you might have a unique key for email addresses to prevent duplicate email entries.

I never realised how much I would need debating skills working in data. Data talks but that doesn't mean people listen. To debate effectively you need to: - Put yourself in your stakeholder's shoes. - Anticipate pushbacks. - Structure your argument around your claim.

Don't aim for this: Excel - 100% SQL - 0% PowerBI/Tableau - 0% Python/R - 0% Aim for this: Excel - 25% SQL - 25% PowerBI/Tableau - 25% Python/R - 25% You don't need to know everything straight away.

Data cleaning is one of the most important skills for a data analyst. Not Excel. Not SQL. Not PowerBI Without clean data, any analysis done is unreliable. Here's my data cleaning 101:

Finally, validate and test. This is the most crucial part. Do some test aggregations and visualisations, and ask yourself: - Does the data make sense? - Does it prove or disprove your theory? - Is there anything that doesn't "feel" right when observing?