Data Analytics & SQL
Excel, SQL, statistics and visualisation — turning data into decisions people act on.
- SQL
- Excel
- Statistics
No account needed to see your path.
Most analytics work is not modelling. It is getting hold of the data, finding out how it is wrong, and presenting what is left in a way that changes what someone decides. SQL and a spreadsheet still do more real analytical work in most organisations than anything more sophisticated.
What you'll be able to do
- Get the data you need out of a database with SQL, including the awkward joins
- Find and handle what is wrong with it before it reaches a chart
- Choose a summary statistic that describes the data rather than flattering it
- Build a visualisation that answers a question instead of decorating a deck
- Say what the numbers support — and what they do not
What happens when you pick this
- 1 Upskili checks what you can already do
- 2 It maps the gap between that and the skill
- 3 It sequences what is left, easiest dependency first
- 4 You practise it, and the path adjusts to how you do
Where this skill is used
Roles that ask for it. Name one when you state your goal and the path is built for that job rather than for the subject in general.
Common questions
Do I need to learn Python to be a data analyst?
Not to start. A great many analyst roles are SQL, a spreadsheet and a visualisation tool, and doing those three genuinely well beats doing four things adequately. Python becomes worth adding once repetition is costing you time or the analysis outgrows what a spreadsheet can hold.
Is SQL still worth learning?
Yes, and it is the single highest-leverage thing in analytics. It has been the way to get data out of a relational database for decades, it has barely changed, and nearly every analytics tool eventually bottoms out in it. Skills with that kind of shelf life are rare.
What is the difference between a data analyst and a data scientist?
An analyst explains what happened and why, usually with SQL, statistics and visualisation, and is measured on whether decisions improved. A data scientist more often builds something predictive, and needs more programming and machine learning. The boundary varies by company, and plenty of roles titled one are the other.
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Band photograph by Mathias Reding on Pexels.