Data Cleaning & Preparation
Missing values, outliers, recoding, merging and reshaping — the unglamorous work that decides whether the analysis is trustworthy.
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Data & Statistics
Python Data Analysis
pandas, statsmodels, scikit-learn and Jupyter notebooks for analysis that has to scale beyond a spreadsheet.
Python suits work where data cleaning is substantial, where the source is an API or a database, or where the analysis shades into machine learning.
We deliver annotated Jupyter notebooks that read as a narrative: what the data is, what was cleaned and why, the analysis, and the conclusion — so a reader who does not code can still follow the reasoning.
What you receive
- Annotated Jupyter notebook or .py modules
- Documented data cleaning and feature preparation
- Statistical or machine-learning analysis with evaluation
- Charts exported at publication resolution
- requirements.txt so the environment is reproducible
Questions about Python Data Analysis
If your department expects SPSS output and your analysis is standard, SPSS is usually the lower-risk choice. Python wins when the cleaning is heavy or the method is not in SPSS. We will give you a straight recommendation for your specific project.
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Thematic, content, framework and grounded-theory analysis in NVivo, MAXQDA or ATLAS.ti — with an audit trail.
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Regression Analysis
Linear, logistic, multinomial, ordinal, hierarchical and multilevel models, with diagnostics that are actually run.
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Mediation & Moderation
PROCESS, bootstrapped indirect effects, moderated mediation and clean interaction plots.
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Next step
Ready to talk about Python Data Analysis?
Send the brief, the deadline and anything you already have. You will get a reference number straight away and a considered reply, usually within one working day.
- Fixed quote agreed before any work begins
- A reference number you can quote on WhatsApp
- Confidential handling, and an NDA if you want one
- An honest answer if your deadline is not realistic
