Analysis, statistics, SQL and data engineering, from the leading course providers.
Google's entry-level data analytics programme: spreadsheets, SQL, Tableau and R, with a portfolio case study.
IBM's beginner programme in data science: Python, SQL, data analysis and visualisation, and machine learning.
Harvard's series on data science with R: basics, visualisation, probability, inference, wrangling, machine learning and a capstone.
A beginner-friendly course on querying data with SQL: filtering, sorting, joins, subqueries and data preparation.
Stanford's course on statistical thinking: descriptive statistics, probability, sampling, regression and hypothesis tests.
A short, free, hands-on course on pandas — the Python library for working with tables of data.
freeCodeCamp's free certification in data analysis with Python, NumPy, pandas, Matplotlib and Seaborn, earned through projects.
A free nine-week course on building data pipelines: containers, orchestration, data warehouses, analytics engineering and streaming.
MIT 6.0002: using computation to understand data — optimisation, simulation, statistics and an introduction to machine learning.
Microsoft's learning path for the Power BI Data Analyst Associate: preparing, modelling, visualising and analysing data.
45 in this topic
Mean, median, mode, spread and shape: the summary numbers every analysis starts with, and when each one misleads.
Data science & analytics 2 min read 6 Mar 2026
The probability ideas analysts use every day: events, conditional probability, independence and Bayes' theorem.
Data science & analytics 2 min read 5 Mar 2026
Normal, binomial, Poisson, exponential and more: recognising the shapes data takes and what they imply.
Data science & analytics 2 min read 4 Mar 2026
Null hypotheses, p-values and significance: what a hypothesis test tells you, and the misunderstandings to avoid.
Data science & analytics 2 min read 3 Mar 2026
How confidence intervals express uncertainty around an estimate, how to read them correctly and why they're more useful than p-values alone.
Data science & analytics 2 min read 2 Mar 2026
How to run a trustworthy A/B test: hypotheses, randomisation, sample size, metrics and the mistakes that invalidate results.
Data science & analytics 2 min read 1 Mar 2026
Why two things moving together doesn't mean one causes the other, the usual culprits, and how to get closer to causal answers.
Data science & analytics 2 min read 28 Feb 2026
Using regression to understand relationships rather than just predict: interpreting coefficients, controls and diagnostics.
Data science & analytics 2 min read 27 Feb 2026
How to make charts that communicate clearly: choosing chart types, honest scales, reducing clutter and highlighting the point.
Data science & analytics 2 min read 26 Feb 2026
A practical guide to matching chart types to questions, with the common charts that are frequently misused.
Data science & analytics 2 min read 25 Feb 2026
How to design dashboards people actually use: clear purpose, the right metrics, logical layout and trustworthy data.
Data science & analytics 2 min read 24 Feb 2026
The SQL every analyst needs: SELECT, WHERE, GROUP BY, JOIN and ORDER BY, with examples.
Data science & analytics 1 min read 23 Feb 2026
Common table expressions and window functions make complex analysis readable: running totals, rankings and period comparisons.
Data science & analytics 2 min read 22 Feb 2026
The pandas operations used in almost every analysis: loading, selecting, filtering, grouping, merging and reshaping.
Data science & analytics 1 min read 21 Feb 2026
Grouping customers by when they started to understand retention and behaviour over time, with a worked approach.
Data science & analytics 2 min read 20 Feb 2026
How to choose metrics that reflect real goals, define them precisely and avoid the traps of vanity metrics and gaming.
Data science & analytics 2 min read 19 Feb 2026
Random, stratified, cluster and systematic sampling — how to draw samples that represent a population, and the biases to avoid.
Data science & analytics 2 min read 18 Feb 2026
Writing clear questions, avoiding bias, choosing scales and analysing responses responsibly.
Data science & analytics 2 min read 17 Feb 2026
Decomposing trends and seasonality, handling calendar effects and comparing periods fairly in business reporting.
Data science & analytics 2 min read 16 Feb 2026
Grouping customers into meaningful segments with rules, RFM analysis or clustering, and making segments useful.
Data science & analytics 1 min read 15 Feb 2026