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Data Science Guides & Tutorials
Roadmaps, salaries, projects and interview prep for aspiring data scientists — from statistics and machine learning to model deployment.
Data Science articles
Recommendation Systems: How Netflix and Amazon Suggest Items
How recommendation systems work, from the ideas behind Amazon's item-to-item filtering and the Netflix Prize to a working recommender built on a real online shop's transactions, then tested honestly against simple baselines, which turn out to be much harder to beat than most tutorials suggest.
How to Become a Data Scientist in 2026 with No Experience
People do start as data scientists with no prior job, but rarely with nothing: in a 2025 survey, data scientists and AI/ML engineers in their first two years had typically been coding for six years, and nearly half were still learning at university. This guide shows what newcomers bring and how to build it.
Kaggle for Beginners: How to Start and Win Medals
Kaggle for beginners: how competitions, leaderboards and the progression tiers work, a realistic first month, the workflow strong competitors follow, and a simulation showing why a public leaderboard can rank a worse model higher, so you learn which score gaps to trust before chasing your first medal.
Probability for Data Science: Concepts You Must Know
Six probability concepts data scientists use, each computed and checked by simulation in Python: why a positive result from a good test can mean only a 9% chance, expected value for decisions, binomial and Poisson counts, why averages become normal, and whether a model's probabilities can be trusted.
Best Free Datasets for Data Science Practice in 2026
Free datasets for data science practice, organised by the skill you want to build: built-in data for learning algorithms, public-sector data for cleaning, large trip records for performance, text and image collections, dataset search engines, and a checklist for licence and quality.
Data Science Career Guide: Roles, Skills and Growth Path
A map of data science careers: what data analysts, data scientists, machine learning engineers, data engineers and analytics engineers actually do, the skills that separate them, realistic routes into a first role, and how people grow from junior work to senior, lead and specialist positions.