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45 guides in Data science & analytics
Measuring conversion through multi-step processes — sign-up, checkout, onboarding — and finding where people drop off.
Data science & analytics 2 min read 22 Feb 2026 For people
Analysing time until an event — churn, failure, conversion — including customers who haven't had the event yet.
Data science & analytics 2 min read 21 Feb 2026 For people
Working with location data: coordinates and projections, joining points to regions, and mapping honestly.
Data science & analytics 2 min read 20 Feb 2026 For people
Turning free-text feedback, tickets and reviews into insight: cleaning, keyword and topic analysis, and using LLMs responsibly.
Data science & analytics 2 min read 19 Feb 2026 For people
Structuring an analysis for busy stakeholders: lead with the answer, show the evidence, state uncertainty and recommend.
Data science & analytics 1 min read 18 Feb 2026 For people
Getting reliable results from spreadsheets: structured tables, lookups, pivot tables and the mistakes that cause costly errors.
Data science & analytics 2 min read 17 Feb 2026 For people
What BI tools such as Power BI, Tableau and Looker do, how they fit with data warehouses, and how to choose.
Data science & analytics 2 min read 16 Feb 2026 For people
Simpson's paradox, regression to the mean, p-hacking, survivorship bias and other traps that lead analysts astray.
Data science & analytics 2 min read 15 Feb 2026 For people
Using Jupyter notebooks without chaos: structure, running order, environments and turning notebooks into reliable pipelines.
Data science & analytics 2 min read 14 Feb 2026 For people
Turning analysis into a narrative that people remember and act on: context, conflict, resolution and the right visuals.
Data science & analytics 1 min read 13 Feb 2026 For people
Updating beliefs with evidence: priors, likelihoods and posteriors explained with practical examples.
Data science & analytics 1 min read 12 Feb 2026 For people
How to work out how much data an experiment needs, and why underpowered studies mislead.
Data science & analytics 1 min read 11 Feb 2026 For people
Why testing many hypotheses produces false positives, and corrections that keep conclusions trustworthy.
Data science & analytics 1 min read 10 Feb 2026 For people
From simple baselines to ARIMA, exponential smoothing and machine learning: choosing forecasting approaches that work.
Data science & analytics 1 min read 9 Feb 2026 For people
Automatically spotting unusual changes in KPIs, and avoiding alert fatigue from false alarms.
Data science & analytics 1 min read 8 Feb 2026 For people
Multivariate tests, switchback experiments, geo tests and quasi-experiments for situations where simple A/B tests don't fit.
Data science & analytics 1 min read 7 Feb 2026 For people
Using language models to write queries, analyse data and create charts — and checking their work.
Data science & analytics 1 min read 6 Feb 2026 For people
The core skills every professional needs to read, question and use data confidently.
Data science & analytics 1 min read 5 Feb 2026 For people
Connecting a company's key outcome to the drivers teams can influence, using metric trees.
Data science & analytics 1 min read 4 Feb 2026 For people
Quick checks to run on any dataset before trusting analysis results.
Data science & analytics 1 min read 3 Feb 2026 For people