Better questions.
Clearer analysis.
Learn SQL, work through Python examples, and build the judgement behind a useful analysis. Start with a guide. Try it for yourself.
Latest guides
16 September 2026AI Data Agents: Build an Evaluation Project for Your Portfolio
Evaluate an AI data agent with answerable, ambiguous and restricted questions. Build a practical accuracy and permissions scorecard.
Read guide ↗Apache Iceberg and Cross-Cloud Analytics: An Interview Guide
Separate table formats, catalogs and query engines, then compare cross-cloud analytics designs with a worked USD cost example.
Read guide ↗Lakebase Snapshots: Practise a Database Restore Interview
Use a synthetic incident to explain snapshots, recovery objectives and restore validation with a concrete timeline.
Read guide ↗Row-Level Security, Column Masking and ABAC: Worked Examples
Distinguish row filters from column masks and build an access test matrix using synthetic sales records.
Read guide ↗Excel and Databricks: Build a Governed Reporting Workflow
Practise grain checks, refresh validation and safe separation of spreadsheet analysis from database write-back.
Read guide ↗How to Check AI-Generated SQL: 7 Mistakes with Examples
Validate AI-generated SQL with seven practical checks covering joins, dates, nulls, metric definitions and reconciliation.
Read guide ↗DuckDB vs Pandas: Analyse the Same Sales Dataset
Run a small sales analysis in DuckDB SQL and pandas, compare matching results, and learn when each approach is useful.
Read guide ↗Analytics Engineer Interview Questions: Models, Tests and Debugging
Practise analytics engineering interview questions about model grain, dbt tests, incremental loads and trustworthy metrics.
Read guide ↗What Is a Semantic Layer? A Revenue Metrics Example
Understand semantic layers through a worked revenue example covering metric definitions, grain, refunds and consistent reporting.
Read guide ↗Power BI Copilot: How to Verify a Report Summary
Check Power BI Copilot summaries against report filters, measures and source totals using a worked revenue example.
Read guide ↗Is Power BI Free or Paid? A Portfolio Builder’s Guide
Choose a setup for learning, presenting a portfolio and sharing a private report. Understand where licensing changes the answer.
Read guideLearn Python for Beginners: Analyse a Sales CSV
Read a small CSV, apply a business rule and verify category totals using only Python’s standard library.
Read guideSQL vs NoSQL: Explain the Difference with an Order Example
Compare a relational order model with a document model, then explain the trade-offs without repeating database myths.
Read guideStructured, Semi-Structured and Unstructured Data: A Practical Guide
Classify tables, JSON, images and text by the information you need to analyse, then turn a mixed support dataset into measurable fields.
Read guideMaven Analytics Data Playground: Choose a Dataset for a Useful Portfolio
Evaluate a practice dataset, write a defensible project brief and avoid producing another dashboard without a clear analytical question.
Read guideStart with a worked example
Learn by doingRevenue dropped.
What changed?
Use SQL to separate traffic, conversion, and order value—and find where to investigate next.
Is your merge doubling revenue?
Catch duplicate customer records, keep missing matches visible, and reconcile your totals.
Read guideA case study you can stand behind
Turn a checkout funnel into a clear portfolio story, with honest assumptions and reproducible calculations.
Read guideBuild your SQL foundations
Open SQL hub ↗Make your JOINs make sense
Understand matching rows, unmatched records, and the questions each join can answer.
Read guideRank, compare, and calculate across rows
Practise rankings, running totals, and comparisons without collapsing your dataset.
Read guideGive your SQL practice a structure
Work through a suggested sequence of fundamentals, joins, and analytical queries.
Read guideWork confidently with data
Open Python hub ↗Pandas methods worth practising
Explore filtering, grouping, reshaping, and other tools for everyday data work.
Read guideThink through an A/B test
Connect hypotheses, metrics, and statistical results to a decision you can explain.
Read guideBuild a dashboard with a purpose
Use project ideas to practise turning business questions into useful reports.
Read guidePrepare for the work—and the interview
View learning path ↗What does a data analyst actually do?
Explore the tasks, tools, and communication involved in analytical work.
Read guideSQL or Python: where should you start?
Choose a starting point that fits the responsibilities of your target roles.
Read guideCompare offers beyond the headline
Separate fixed pay, variable pay, and one-time bonuses with a worked example.
Read guideNew: AI tools and analytics measurement
Two practical guides published 17 September 2026.
- MCP for Data Analysts: How dbt Connects AI to Trusted Metrics
Understand the connection, metric definitions and checks behind an AI answer.
- How to Track AI Referral Traffic in GA4
Build a tested source classification and distinguish visits from AI visibility.
New US analytics exercise
Calculate daily active users across US time zones
Run a Python example covering midnight, repeat users and a 25-hour daylight-saving day. Published September 18, 2026.
New product analytics exercise
Day-7 retention in SQL: count eligible users correctly
Run a tested cohort query and catch duplicate events, missing returns and incomplete observation windows. Published September 18, 2026.