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Analytics vocabulary that every Data Analyst should know.
(Save it now so you don't have to search for it later)
1. Exploratory Data Analysis = poking around a dataset to understand it
2. Feature Engineering = making new columns from existing ones
3. Dimensionality Reduction = squishing many columns into fewer
4. Cohort Analysis = tracking groups over time based on when they joined
5. Funnel Analysis = counting how many people drop off at each step
6. Attribution = figuring out which marketing effort deserves credit
7. A/B Test = showing two versions to two groups and comparing outcomes
8. Statistical Significance = unlikely to be random chance
9. Confidence Interval = our best guess, plus a range around it
10. Segmentation = splitting users into meaningful groups
11. Retention = who came back
12. Churn = who didn't
13. LTV (Lifetime Value) = how much a customer is worth over their whole relationship
14. CAC (Customer Acquisition Cost) = how much it costs to get one new customer
15. MoM / QoQ / YoY = month/quarter/year over year
The field loves complex names for simple things.
Understanding the plain-English version is 80% of feeling comfortable in analytics conversations.
𝐏.𝐒. If you're trying to build a career in data, you can learn more about Data Career School here → www.datacareerschool.com
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You've been telling yourself you'll figure out AI once you've nailed the basics.
Meanwhile job postings for data roles in 2026 read like this:
→ SQL, Python
→ Cloud (any major one)
→ Experience working with LLMs / AI tools a plus
→ Comfortable with modern data stack
→ Familiarity with vector databases / RAG / agents preferred
The basics goalpost keeps moving.
It will keep moving in the future.
The people getting hired right now aren't the ones who waited until they felt fully ready.
They're the ones who learned in public.
Shipping small AI-integrated projects,
posting about them,
getting feedback, iterating.
If you've been waiting for the "right time" to add AI to your skillset
There isn't one.
There's just before and after.
𝐏.𝐒. If you've spent months learning but aren't sure how to turn those skills into opportunities, you can learn more here → www.datacareerschool.com
