100 Must-Know Analytics Terms for MBA Interviews - Complete Glossary
A product manager opens a morning dashboard and sees the same story in three languages: conversion is down, churn is rising, and one customer segment is behaving strangely. The analytics student who can translate those signals into terms like cohort, funnel, precision, recall and leakage instantly sounds business-ready.
- Analytics is decision support: data becomes useful only when it changes a business action.
- Think in a funnel: raw data - clean signal - model - decision - business value.
- The must-know terms fall into clusters: data basics, statistics, probability, data prep, EDA, SQL/BI, machine learning, model types, evaluation metrics and business analytics.
- For classification models, revise confusion matrix, accuracy, precision, recall, F1 and ROC-AUC.
- For regression and forecasting, revise MAE, RMSE, R-squared and MAPE.
- The strongest interview answers define the term, give a business example, name the metric and mention one risk such as bias, leakage or base-rate error.
- The biggest trap is memorising definitions without connecting them to a decision.
The Big Picture: Analytics Turns Data Into Decisions
Do not revise analytics as 100 disconnected definitions. Revise it as a value chain: a business collects raw data, cleans it into reliable signals, models patterns, makes a decision and then measures whether the decision improved the business.
Core Explanation: The 10 Buckets Behind the 100 Terms
The fastest way to master this glossary is to group terms by job-to-be-done. Some terms describe the raw material, some describe statistical thinking, some describe data preparation, and some describe model performance. In interviews, the same term should move from definition to business use.
Definitions: The Few You Must Say Perfectly
- Analytics: Systematic analysis of data to discover patterns, explain outcomes and improve decisions.
- Metric: A quantified measure used to track performance, behaviour or model output.
- KPI: A metric tied directly to a strategic business objective.
- Correlation: A statistical measure showing how two variables move together, without proving causation.
- p-value: The probability of observing results this extreme if the null hypothesis were true.
- Machine learning: Algorithms that improve task performance by learning patterns from data.
The 100 Must-Know Analytics Terms
Use this glossary like a placement cheat sheet: cover the term, meaning and business usage. If you can attach one example to each cluster, you will sound practical rather than theoretical.
The Metrics Recruiters Expect You to Know
When an interviewer moves from terms to application, they often test whether you can choose the right metric. The trick is to match the metric to the business cost of error.
The Error Matrix That Makes Classification Click
Most students know accuracy; strong candidates know what kind of wrong answer the business can tolerate. A fraud model and a product recommendation model should not be judged the same way.
A Small Worked Example: Precision, Recall and F1
Suppose a bank model flags 100 customers as likely defaulters. Out of these, 70 actually default. There were 90 actual defaulters in total.
The business interpretation matters: if missing a defaulter is very costly, the bank may accept lower precision to increase recall.
Case Study: Ather Energy - Analytics in a Connected EV Business
Ather Energy shows how analytics becomes a product capability when connected scooters, app behaviour, service data and battery telemetry feed a continuous decision loop.
Situation: Electric two-wheelers are not only vehicles; they are connected products. Ather scooters generate operational signals through ride behaviour, battery performance, charging patterns and service interactions. For an EV company, these signals matter because customer trust depends on range, reliability, charging experience and after-sales service.
The move: Ather has built its business around a connected product experience: scooter software, app-based ownership, service diagnostics and charging ecosystem support. Analytics can be applied to time-series telemetry, anomaly detection, cohort behaviour, service planning and product improvement. The primary driver is the connected data loop from vehicle to software to service decision. Supporting drivers include in-house product-software integration, over-the-air update capability, dealer and service touchpoints, and a focused premium EV customer segment.
Outcome or lesson: The lesson is not that analytics alone wins. Analytics creates advantage when the company has data access, operational teams that act on insights, and feedback loops that improve the product experience.
So what: A complete analytics answer connects data, model, decision and business impact. In Ather's case, the strategic point is that connected-product analytics improves reliability, service planning and customer experience when supported by execution systems.
How AI Changes 100 Must-Know Analytics Terms in 2026
AI does not remove the need to know analytics terms. It raises the bar because managers can now generate charts and models quickly, but still need to judge whether the output is statistically and commercially sound.
Interview Relevance
"Explain any analytics project or dashboard you have worked on. What were the key terms, metrics and business decisions involved?"
If you forget a term, do not panic. Say what it measures, where it appears in the analytics workflow, and why it matters to the business decision.
Common Mistake
Defining terms like a dictionary and stopping there. It costs candidates because analytics roles test judgement, not memory. The fix: for every term, add one business example and one decision implication - for example, "Recall matters in churn because missing a high-risk customer means losing the chance to retain them."
What to Revise Next
This is the final lesson in the analytics course, so your best next move is a capstone review. Pick one company, build a one-page analytics story around it, and revise the full journey: business problem, dataset, EDA, model, metric, dashboard, action and risk.