Ideas that turn data into decisions.
Hands-on guides across analytics, BI, statistics, data science and AI — written by people who ship dashboards and train models for a living, not just blog about them.
The 2026 analytics stack: from spreadsheets to LLMs — without the chaos
Fresh from the studio
Practical, sharply edited guidance across BI, statistics, data science, AI and learning to do it yourself.
Power BI vs Tableau vs Looker: which BI tool actually fits your team
A no-hype comparison by cost, learning curve, data modelling and sharing — so you pick the dashboard tool you'll still be happy with in two years.
Read moreFrom SPSS to R: making the jump without losing your statistics
Map every menu you rely on in SPSS to a few lines of R — t-tests, regressions, ANOVA and clean reporting — and finally make your analysis reproducible.
Read more8 SQL patterns every data analyst should know by heart
Window functions, CTEs, conditional aggregation and de-duplication — the queries that turn a junior analyst into the person the team asks first.
Read morePython + Jupyter: the analyst's notebook workflow, end to end
Load, clean, explore and visualise a messy dataset with pandas — a repeatable notebook structure you can reuse on every project you touch.
Read moreA practical intro to LLMs for analysts (no PhD required)
What large language models can and can't do with your data, where NLP fits, and how to prototype something useful this week — safely.
Read moreHow to actually learn data analytics in 2026 — a coaching roadmap
A realistic 90-day path from spreadsheets to SQL, a BI tool and your first portfolio project — plus how 1:1 coaching cuts the time in half.
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