Timed, interview-style tests across SQL, Python, and analytics. Each attempt draws random questions from a large bank — so you can practice again and again.
Test your understanding of basic SQL concepts with these MCQs and see how you score
A comprehensive practice set containing 25+ SQL coding problems covering SELECT, WHERE, ORDER BY, LIMIT, DISTINCT, CASE WHEN, and NULL handling. Complete this to build solid muscle memory for SQL query writing before proceeding to SQL Advanced.
If you can crack this one, you have 90 percent chance of clearing your next SQL test and interview.
Master the SQL basics every data role requires. This test covers SELECT, filtering, sorting, aggregations, and basic joins using a music store database. Perfect for beginners preparing for their first data interview.
Tackle real-world analytics scenarios using an e-commerce database. You'll write queries for customer segmentation, revenue analysis, and product performance — the exact type of questions asked in data analyst interviews.
Demonstrate senior-level SQL skills with SaaS metrics analysis. Calculate churn rates, cohort retention, MRR trends, and funnel conversions — the metrics every SaaS company cares about.
Write complex queries across multiple e-commerce tables. Covers multi-table joins, correlated subqueries, conditional aggregation, and business reporting patterns used in production environments.
Push your SQL skills to the limit with advanced window functions, recursive CTEs, running totals, and complex analytical queries on SaaS data. For experienced developers targeting senior roles.
Build your pandas foundation with hands-on coding challenges. Filter DataFrames, compute aggregations, handle missing data, and create new columns — essential skills for any Python data role.
Go beyond basics with groupby operations, merge/join, pivot tables, and time series analysis. Solve realistic data manipulation challenges that mirror actual interview assessments.
Solve data engineering SQL challenges: build staging queries, handle incremental loads, deduplicate records, and compute running aggregations on SaaS data. Designed for data engineer interviews.
Write Python code for real ETL scenarios: data validation, transformation pipelines, schema mapping, and aggregation logic. A hands-on test for data engineers who build pipelines in Python.
The comprehensive qualifying exam for the Data Analyst track — SQL, Python, statistics, Excel, visualization and data modeling in one sitting. Score 80%+ to qualify for the Job-Ready certificate.
The Phase 1 exit gate. Ten interview-grade SQL problems on a SaaS analytics dataset — sessionization, cohort retention, NRR, percentiles, anti-joins, gaps-and-islands, running totals and top-N per group. Pass this and nobody can question your SQL.
The cumulative exit test for Phase 1's SQL weeks -- every SQL concept taught from SQL Basics through SQL Advanced, MCQ and coding both.
Timed scenario-based assessment evaluating descriptive spread, statistical inference, A/B testing mechanics, stats traps, and product metrics.
Battle-tested 45-minute SQL interview benchmark calibrated to the Round 1 screening bars of Amazon, Google, Meta, Uber, and Microsoft. Tests complex window functions (DENSE_RANK, LEAD/LAG), gaps-and-islands sessionization, MRR waterfall modeling, and multi-CTE category aggregations.
High-scale product analytics SQL benchmark calibrated to Tier 2 scale-ups (Flipkart, Swiggy, Zomato, Razorpay, Zepto, Meesho). Tests customer re-order velocity, RFM spend quartile segmentation (NTILE), payment method checkout performance, and category profit margin aggregations.
Analytical SQL screening simulator calibrated to FinTech and growth leaders (CRED, Paytm, Groww, PhonePe, BrowserStack). Tests invoice settlement efficiency, acquisition channel ARPU monetization, subscription churn concentration, and multi-table ledger aggregations.
Standard technical screening benchmark calibrated to Enterprise IT and management consultancies (TCS, Infosys, Wipro, Accenture, Deloitte, EY). Tests core multi-table joins, anti-joins, conditional CASE WHEN segmentation, and GROUP BY / HAVING volume aggregations.
Comprehensive 5-level step-up diagnostic ladder evaluating candidates across all industry tiers in a single session. Spans from Tier 4 core joins to Tier 1 multi-CTE conversion latency modeling, computing your exact First-Round Survival Probability Index and 5-pillar skill breakdown.
Timed, scenario-driven certification exam for dbt Core and Cloud.
Timed scenario-based exit assessment for Statistics for Data Science (mini: 25-question short form): foundations, inference, regression, Bayesian reasoning, and causation/experimentation.
Timed, scenario-driven certification exam: bias-variance, CV design, leakage prevention, metric selection, regularized modeling.