Overview
Researchers in computer science, engineering, the natural sciences and increasingly the social sciences analyze data in Python because it connects easily to web data, sensors, text and databases. The scientific stack — NumPy, pandas, SciPy, statsmodels, scikit-learn and matplotlib or seaborn — covers most needs, but each library answers a different kind of question, and mixing them without care leads to results that look precise and mean little.
A frequent example: fitting a regression in scikit-learn and then looking for p-values that it does not provide, because scikit-learn is built for prediction rather than inference. We match each research question to the right tool — statsmodels or SciPy for estimation and hypothesis tests, scikit-learn for prediction with honest validation — and explain the difference in your write-up.
Work is delivered as Jupyter notebooks, Python scripts or a small project package, with a pinned environment file. You can have the analysis delivered and explained, have your own code reviewed, or learn by writing it with us.
Who it’s for and the problems it solves
This is for you if you are…
- Computer science and engineering students adding statistical analysis to a thesis project
- Researchers with large, web-scraped, sensor or log data that is awkward in point-and-click software
- Social and health scientists moving from SPSS or Excel who want free, scriptable tools
- Early-career researchers combining classical statistics with a predictive model
Common problems we help with
- Notebook cells were run out of order and results cannot be reproduced from a fresh kernel
- pandas code written for older versions now fails or behaves differently, for example chained assignment
- You need p-values, confidence intervals or robust standard errors, but your model is in scikit-learn
- Model accuracy looks excellent but data leakage from preprocessing or duplicate records is suspected
- Dates, time zones and mixed-type columns cause parsing errors or silent mistakes
- Your environment works on your laptop but not on a server or your coauthor's machine
What’s included — and what isn’t
Included
- Environment setup with venv, conda or uv, and a pinned requirements.txt, environment.yml or lockfile
- Loading CSV, Excel, Parquet, SPSS or Stata files and SQL queries into pandas DataFrames
- Cleaning and reshaping with pandas: types, missing values, merges, groupby aggregations, melt and pivot, and datetime handling
- Statistical inference with statsmodels (OLS, GLM, mixed models, formula interface) and SciPy tests
- Predictive modeling with scikit-learn Pipelines, cross-validation, hyperparameter tuning and held-out test sets
- Diagnostics: residual plots, influence measures, calibration curves and confusion matrices as appropriate
- Figures with matplotlib or seaborn styled for publication, and tables exported to CSV, Word or LaTeX
- Notebooks restructured so they run top to bottom, with key logic moved into functions or scripts
- Random seeds set for any stochastic step, and a README explaining how to run the analysis
Not included
- Training models or tuning thresholds on the test set to improve reported performance
- Writing the thesis or article text you submit as your own
- Building production software, dashboards or web applications beyond the analysis
- Scraping websites in breach of their terms of use or collecting personal data without a lawful basis
What you receive
- Analysis notebooks or scripts
- Jupyter notebooks that run cleanly from a fresh kernel, or numbered .py scripts, with markdown explanations between steps.
- Environment file
- A requirements.txt, environment.yml or uv lockfile listing the Python and library versions used.
- Results outputs
- Model summaries, tables and figures saved to an outputs folder by code, not copied from the screen.
- Methods notes
- A short plain-language explanation of which models were used, why, and how their output should be read.
- Walkthrough session
- A live session to go through the code and results so you can extend or defend them.
How it works
Scoping
We discuss your questions, data sources and whether the goal is inference, prediction or both, since that shapes the tools.
Environment and data load
We create an isolated environment, load the data and profile it to understand types, missing values and scale.
Cleaning and features
Transformations are written as functions or pipeline steps so they can be reused and applied consistently.
Modeling and validation
We fit the agreed models with appropriate validation and diagnostics, and review the results with you.
Packaging and handover
You receive notebooks or scripts, the environment file, outputs and a walkthrough.
Libraries and approaches we use in Python
- pandas and NumPy
- DataFrame cleaning, joins, reshaping and aggregation, written for pandas 3 behavior (Copy-on-Write and the default string dtype) or your installed version.
- statsmodels
- Formula-based OLS, GLM, logit/probit, mixed linear models and time series, with robust or clustered covariance and full inferential summaries.
- SciPy and pingouin
- t tests, non-parametric tests, correlations and effect sizes when a simple test answers the question.
- scikit-learn
- Pipelines with ColumnTransformer, cross-validation, grid or randomized search, and metrics suited to imbalanced data.
- Visualization
- matplotlib for full control and seaborn for statistical graphics, exported at journal resolution.
- Scale and performance
- Parquet files, chunked reading, and Polars or DuckDB when data are too large for comfortable pandas use.
Inference and prediction are different jobs
If your research question is whether a variable is associated with an outcome, and by how much, you need estimates with standard errors and confidence intervals. statsmodels provides these in a familiar regression table and supports formula syntax similar to R. If your question is how well an outcome can be predicted for new cases, scikit-learn's cross-validation and held-out testing are the right framework, and coefficients are secondary.
Many theses need both: an explanatory model for hypotheses and a predictive model for a practical application. We keep them clearly separated in the code and in the explanation, so an examiner does not see a regularized coefficient reported as if it were an unbiased estimate.
Notebooks that tell the truth
Jupyter notebooks are ideal for exploring and explaining, but hidden state is a real risk: a cell edited after running, a variable defined in a deleted cell, or cells executed out of order. Before handover, we restart the kernel and run every notebook from top to bottom, and we move longer functions into a .py module that the notebook imports.
Outputs such as tables and figures are written to files by code. That way the numbers in your chapter can be traced to a specific run rather than to a screenshot from weeks ago.
Pitfalls we check for
Some Python mistakes raise no error at all, so we look for them deliberately.
- Data leakage: scaling, imputation or feature selection fitted on the full dataset before splitting
- Chained assignment such as df[df.x > 0]['y'] = 1, which no longer modifies the DataFrame under Copy-on-Write
- Integer columns silently converted to floats after missing values appear, changing identifiers
- Merges on keys with different types (string versus integer) that match nothing
- Random splits applied to grouped or time-ordered data where grouped or time-series cross-validation is needed
Library versions and environments
The scientific Python stack changes quickly. pandas 3.0, released in January 2026, made Copy-on-Write the only behavior and introduced a dedicated string dtype, so some older code needs updating. scikit-learn releases new minor versions regularly (the 1.9 series is current as of this writing), and statsmodels updates less often and occasionally needs a patch release to stay compatible with new pandas or NumPy versions.
We therefore pin every project. A requirements file or conda environment records exact versions, and newer tools such as uv can produce a lockfile that recreates the environment precisely. If your code must run on an older institutional server, we test against those versions instead.
What the work can look like
Illustrative example — fictional data
Sample review note: leakage in a dropout-prediction model
An education researcher predicted course dropout from learning-platform logs for 3,900 students and reported an AUC of 0.94. The values are invented to show what a review note looks like.
Issue found
Missing values were imputed and features standardized on the full dataset before the train-test split, and one feature, 'days_active_total', included activity after the prediction date.
Correction
Preprocessing was moved into a scikit-learn Pipeline fitted within each cross-validation fold, the post-cutoff feature was rebuilt using only data up to week 4, and students were split by cohort to avoid overlap.
Revised result
Cross-validated AUC fell to 0.78 (SD across folds 0.03), with a calibration plot and a confusion matrix at the chosen threshold. The note explains why the lower figure is the honest estimate for new cohorts.
Illustrative example with invented data and metrics. Real reviews depend on your data, design and reporting standards.
What you’ll need to provide
- Data files or database access instructions, with any documentation
- Research questions and whether you need inference, prediction or both
- Existing code and the full traceback of any errors
- Your Python version and how you manage environments (Anaconda, pip, uv)
- Output requirements: figure specifications, table style and deadline
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Size and messiness of the data, including number of sources to join
- Whether the work is inferential, predictive or both
- Model complexity and validation design (nested or grouped cross-validation, for example)
- Deliverable format: notebooks only, scripts plus report, or a packaged project
- Delivered analysis, code review or tutoring sessions
Turnaround depends on
- Data access and documentation at the start
- Compute time for tuning or large datasets
- How quickly questions about variable definitions can be answered
Every project is quoted individually. How we price our work.
Questions people ask before starting
Should I use Python or R for my research?
Both are capable. Python is often better for data engineering, text and machine learning; R has deeper coverage of specialist statistical methods. Follow your supervisor or lab when possible. We work in either and can combine them if needed.
Can you get p-values from my scikit-learn model?
scikit-learn is not designed for that. We refit the explanatory model in statsmodels, which gives standard errors, confidence intervals and p-values, and keep scikit-learn for predictive evaluation.
Do you work in Google Colab?
Yes. We note the library versions Colab provided at the time, since Colab updates them, and supply a requirements file so the work can also run locally.
My old pandas code gives warnings or errors now. Can you update it?
Yes. We identify changes from pandas 2 to 3, such as chained assignment and string dtypes, update the code and confirm the outputs match the earlier run where they should.
Is the code mine to reuse?
Yes. You receive all code and can reuse and adapt it for your project and later work, while acknowledging analytical assistance where your institution requires it.