✦Data, AI & ML

Data Analyst

Practice SQL, Excel, statistics and dashboard questions that Data Analyst interviews ask, from basic joins to A/B tests and metric design.

Questions: 100
Interview Qs: 30
Judged problems: 44
Skills: 9

Start practising

About the role

A Data Analyst turns raw business data into answers people can act on. On a normal day you write SQL to pull data from a warehouse, clean it in Excel, Python or a BI tool, and build charts and dashboards that show how the business is doing. You are often the person a manager asks: why did sales drop last week, which campaign worked, or which customers are about to leave.

The job is part technical and part communication. You need to know SQL well, understand basic statistics, and be careful about data quality, because one wrong join can double a revenue number. Just as important is asking the right question first, choosing the right metric, and explaining the result in simple words to people who do not read SQL.

As you grow, you own key metrics for a team, design experiments such as A/B tests, build self-serve dashboards, and work closely with data engineers on the pipelines that feed your reports.

Practise by level

easy

Easy challenges

34 questions

medium

Medium challenges

34 questions

hard

Hard challenges

32 questions

Open the full question bank (100 questions) →

Mock interview

easy

Screening round

4 questions · 4 min each · pass 50%

medium

Technical round

5 questions · 4 min each · pass 60%

hard

Final round

6 questions · 3 min each · pass 70%

SQL problems

All SQL problems →

Skills in this track

Learning roadmap

1
Spreadsheets and basic statistics
Learn Excel or Google Sheets well: formulas, XLOOKUP, pivot tables and charts. Learn mean, median, percentiles, standard deviation and why outliers matter.
2
SQL from basics to window functions
Practice SELECT, WHERE, GROUP BY, HAVING, all JOIN types, subqueries, CTEs and window functions like ROW_NUMBER and running totals on real datasets.
3
Visualization and dashboards
Pick one BI tool such as Power BI, Tableau or Looker Studio. Learn which chart fits which question and how to design a clean dashboard for a busy manager.
4
Python for analysis
Use pandas for cleaning and joining data, and matplotlib or seaborn for charts. Learn to work in Jupyter notebooks and keep your analysis reproducible.
5
Experiments and business metrics
Understand funnels, cohorts, retention, conversion rate and A/B testing, including sample size, p-values, confidence intervals and common traps.
6
Portfolio and storytelling
Build 3 to 4 end-to-end projects that start from a business question and end with a clear recommendation. Practice explaining them in under two minutes.