Pre-Interview Cheatsheet
Data Analyst — Confidence Cheatsheet
A printable, focused refresher tuned for Data Analyst. Open the sections that matter to you and walk in confident.
Tuned for Data Analyst · Business, Finance & Analytics > Data & AnalyticsRefresh Right Now The 60-second mental warm-up before you start.
- Know the analysis workflow: define question, locate data, clean data, explore, validate, analyze, visualize, communicate.
- Refresh SQL: SELECT, WHERE, GROUP BY, JOIN, HAVING, window functions, CTEs and aggregation traps.
- Know Python/pandas basics: DataFrame, filtering, groupby, merge, missing values, duplicates, datatypes and plotting.
- Understand statistics basics: mean/median, variance, correlation vs causation, sampling bias, confidence intervals and A/B tests.
- Strong analysts start from the business decision, not from the tool.
Core Vocabulary Terms interviewers expect you to use precisely.
- ETL/ELT: extracting, transforming and loading data between systems.
- Data cleaning: fixing missing, inconsistent, duplicated or invalid records.
- Metric definition: exact rule for calculating a KPI.
- Outlier: unusual value; may be error or real signal.
- Data lineage: where data came from and how it was transformed.
Formulas & Frameworks The mental models that organise your answers.
- Analysis flow: business question -> metric definition -> data source -> quality check -> analysis -> insight -> recommendation.
- SQL check: row count before/after joins, null checks, duplicate keys, aggregation level.
- Visualization rule: choose chart based on comparison, trend, distribution or relationship.
- Good output: answer, evidence, caveats, action.
Likely Interview Prompts Questions you should be ready for.
- How do you clean a messy dataset?
- Explain a JOIN and common mistakes.
- How would you investigate a drop in conversion rate?
- What is correlation vs causation?
- Describe a dashboard you built and the decision it supported.
Red Flags To Avoid Common answers that lose interviews.
- Jumping directly to code without clarifying the business question.
- Not checking data quality.
- Making causal claims from correlation.
- Using averages when medians or distributions matter.
- Building pretty dashboards with unclear metric definitions.
What Sets You Apart Signals that move you from competent to memorable.
- Mentions validation and edge cases.
- Can explain technical results in business language.
- Knows enough statistics to avoid false conclusions.
- Documents assumptions and reproducible steps.
30-Second Confidence Reset Anchor sentence to read just before you walk in.
I should sound like a decision analyst, not only a dashboard builder: define the question, validate the data, find the driver, explain the action.