The degree sits between two fields and is assessed on both
Data science students usually arrive strong in either statistics or engineering and are assessed on both. The gap, wherever it is, tends to show up in the same place: a technically competent project whose conclusions are not supported.
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Two different gaps, both visible in the write-up
Students from a computing background build clean pipelines and reach conclusions their analysis does not support, usually by treating correlation as causation or by ignoring uncertainty. Students from a statistics background reason carefully and struggle with reproducibility, versioning and making the work runnable by someone else.
Both gaps appear in the report rather than in the code. A project that states findings without confidence intervals, or that cannot be rerun from the repository, loses points in a predictable place regardless of how good the underlying work is.
The capstone then adds a third demand: communicating to a non-technical stakeholder. That is a separate skill from doing the analysis, and it is frequently weighted heavily because it is what the job actually requires.
- Uncertainty reported, not just point estimates
- Causal language matched to the design
- Work reproducible from the repository
- Findings communicated for a non-technical reader
What we work on
Your own project and your own data
- Statistical inference and reporting uncertainty properly
- Experimental design and A/B testing coursework
- Reproducibility: environments, seeds and documentation
- Data visualization that supports the claim being made
- Capstone scoping, write-up and stakeholder presentation
We explain. You do the analysis
- We do not write code, notebooks or reports you will submit.
- We do not run your analysis or produce results.
- We do not fabricate or alter data.
- We do not complete assignments or supply solutions.
- We work through your own project and review the write-up.
Presenting results you did not produce is fabrication rather than plagiarism. It is also the error most likely to be caught, because the code and the claimed output have to agree. Read the full policy.
Frequently Asked Questions
Confidence intervals or credible intervals alongside point estimates, and a statement of what they mean in the units of the problem. A single number with no indication of precision invites a reader to treat it as exact, which is almost never warranted.
When the design supports it: a randomized experiment, or an observational design with an explicit identification strategy you can defend. Observational data with controls added is not sufficient, and this is the most common correction on data science reports.
That someone else can rerun your analysis and get your numbers. In practice: pinned dependencies, fixed random seeds, data access documented, and a script or notebook that runs end to end. Many rubrics now test this by actually trying.
Lead with what you found and what it means for a decision. Put the method in an appendix. Use one chart that makes the point rather than six that show your work. The instinct to demonstrate rigor up front is what makes these presentations fail.
Report them as inconclusive and explain what would be needed to resolve it. An honest account of an underpowered or confounded analysis is a legitimate capstone. Overstating a weak finding is the outcome that actually costs points.
Send the project and the brief
We will tell you whether the conclusions are supported, whether the work is reproducible, and whether the write-up would land with a stakeholder.
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