From the data analysis archive
Practice datasets I collected, with questions to work through, plus the guides and checklists I made for data projects. The analysis is only half the work. The other half is explaining what you found, so each project asks you to do both.
Each dataset is on Kaggle, with a documentation page that explains the columns and gives more detailed steps.
Real freelance jobs posted online, with job titles, price ranges, required skills and client details. Use it to explore trends in freelance work and practice breaking down project details.
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Stars, forks, languages and activity for more than 14,000 public GitHub repositories. Good practice for data cleaning, trend analysis and telling a story with real tech data.
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Real UK property sales with their sale history. See how house prices changed over time, which types of homes sold most, and how often people moved. You'll practice joining tables and working with dates.
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Each one opens as a Google Doc you can read or copy.
Every step from raw file to clean file, so you catch errors before the analysis starts.
Explain what you did, how you did it and what it means.
Run it before you share. It catches slow pages, hidden labels and weak color choices.
Answer a few questions and it points you to Excel, SQL, Tableau or Python as a place to start.
A simple structure for your project story, so recruiters see the value without digging.
Checks for data, code, visuals and documentation before you share a project.
Say why the project matters, what the data shows and what comes next.
Tune your profile so search and recruiters find what you do.
Record every column's name, type and meaning, so nobody has to guess.