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Insight—not code volume—is the value data science is meant to create. Coding agents can make it faster to turn an idea into executable code, but a working implementation does not show that the analysis asked the right question, used appropriate evidence, or supports its conclusion. The hard work remains: understand the data, choose a defensible method, and make the reasoning reviewable.
Why working code is not the same as a sound analysis
A passing test can confirm that software behaves as specified. It cannot, by itself, establish that the specification captures the real problem or that the data and method justify an interpretation. Code is a means of making an analysis executable; the analysis’s value lies in what it helps people learn and whether that learning is supported by evidence.
Andrew Hinton, author of the September 30, 2026 article, puts the reviewer’s need plainly: “I want to understand the question, what we found, and whether the evidence supports the conclusion.” That is a different standard from asking whether a code diff is tidy or a notebook runs without errors.
Hinton’s argument is an editorial view about how data science work should be valued, not a measured finding that coding agents raise productivity, improve insight quality, or produce more discoveries. The available article copy identifies him as its author; the original Towards Data Science page could not be fetched directly, so the attribution and framing are based on that accessible copy.
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What data scientists still need to do
Formulate a useful question
Before choosing a model or writing code, make clear what decision, explanation, or uncertainty the analysis addresses. Define the population or cases in scope and what result would count as informative. An agent can implement instructions quickly, but speed cannot rescue a question that is vague or disconnected from the decision at hand.
Understand how the data came to exist
Observations are products of measurement and collection processes. Missing values, outliers, group definitions, and changes in instrumentation may reflect meaningful behavior—or quirks in how the data was recorded. Analysts need to investigate those possibilities rather than treating a dataset as a neutral, self-explanatory input.
Domain understanding does not have to reside entirely in one data scientist. Collaborating with people who know how the measurements were produced can reveal context that is invisible in the table. Programming and statistical skill remain important, but so does knowing when to ask someone who understands the underlying process.
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A method should fit the question and the data, with relevant assumptions made visible. Generated code may faithfully implement a chosen method while the choice itself is inappropriate, or may produce a result that answers a narrower question than the one stakeholders care about. The analyst must judge the fit, inspect the result, and state what it does—and does not—establish.
Make analysis reviewable, not just executable
A useful review record connects the reasoning to the computation. It should let a reviewer trace the path from question to evidence and challenge assumptions without having to infer the analysis from code alone.
- Question and scope: the hypothesis or decision, and the population or cases it concerns.
- Data: source and version, important transformations, and definitions of measures or groups.
- Method: the approach, relevant assumptions, and why it suits the question.
- Evidence: figures and results, with uncertainty and limitations made clear.
- Interpretation: what the findings support, what they leave unresolved, and how they relate to the original question.
- Execution record: enough information to rerun the computation when that matters, while distinguishing computational reproducibility from scientific validity.
A notebook can bring these pieces together, but it is not the only option. An experiment interface or executable report can serve just as well if it exposes equivalent evidence and context. A clean rerun helps establish that the computation can be repeated; it does not prove the conclusion is sound.
Separate exploration from work offered for acceptance
Exploration benefits from room to change direction. Analysts may try alternatives, investigate surprising behavior, and refine the question as they learn. That exploratory record need not make every experiment look like a final result.
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When a change is presented for acceptance, however, reviewers need a coherent account of what prompted it, which alternatives were considered, and what evidence supports the chosen conclusion. Keeping that distinction clear protects experimentation while making consequential claims assessable.
How to evaluate a coding agent on analytical work
Agent evaluation starts by defining tasks and success criteria. Anthropic’s January 9, 2026 engineering guide calls each attempt a trial and recommends using multiple trials when outcomes vary. It also describes code-based, model-based, and human graders, whose suitability depends on the outcome and behavior being assessed. Its guide is practical advice for evaluation design, not evidence that every recommendation applies identically to every data science workflow.
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Anthropic suggests 20–50 simple tasks as a reasonable starting point for early evaluations built from real failures. That is a practical starting recommendation, not a universal sample-size guarantee. The same guide reports language-model performance on SWE-bench Verified rising from 40% to more than 80% in one year; that figure belongs to that benchmark and period, and is not a measure of general coding-agent quality or data science productivity. Anthropic summarizes the purpose of evaluation this way: “Good evaluations help teams ship AI agents more confidently.”
Define what success means for the task
A unit test can establish that a specified behavior works. For analytical work, success may also depend on whether the result answers the intended question, handles realistic inputs, respects methodological constraints, or produces an interpretable explanation. Define the target before comparing systems; otherwise a high pass rate may measure only what was easiest to test.
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Run enough trials to see variability
When an agent’s output is stochastic, a single successful attempt says little about reliability. Report the conditions and whether the evaluation concerns a best-case result, repeated performance, or another task-specific goal. Multiple trials help show how often the agent succeeds and what kinds of failures recur.
Inspect outcomes and traces
Outcome scores show whether a task met its criteria; transcripts or other traces help explain how the agent got there and where it failed. A reviewer should be able to see enough context to interpret both. For an agent study, record the model, instructions, tool versions, environment, task set, trial conditions, grading criteria, outcome data, and relevant traces or transcripts. Include latency or cost only when those operational measures matter to the intended use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical standard for data science pull requests
A pull request for analytical work should make review possible at two levels: whether the implementation behaves as intended and whether the analysis supports the claims attached to it. A reviewer can use these questions to structure the discussion:
- Is the question specific, and is its scope clear?
- Can the reviewer identify the data version, key transformations, and definitions that shape the result?
- Are the method and its important assumptions visible, with a reason they fit the question?
- Do figures and results show uncertainty or limitations that affect interpretation?
- Does the written conclusion stay within what the evidence establishes?
- Can the computation be rerun where necessary, and can the reviewer access the data needed to inspect it?
Tools can support this record, but no platform feature substitutes for it. Databricks documentation, for example, describes notebook source and output formats and Git-based job execution. Those capabilities may help preserve workflow context; they do not, on their own, establish reproducibility, reviewer access, or sound interpretation.
Do data scientists need to be strong programmers?
They need enough programming skill to understand, inspect, and challenge the code their work depends on. Coding agents may reduce the effort of translating an idea into implementation, but they do not remove the need to recognize whether that implementation matches the intended analysis. Statistical and methodological judgment, data understanding, and domain context remain essential parts of the job.
For readers who want a foundation in data-analytic thinking, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett is a relevant book. NYU Stern’s 2013 page described it as a textbook used by more than a dozen universities in eight countries at that time; this is a historical adoption figure, not a current count. NYU Stern describes the book as covering principles for extracting knowledge from data and evaluating data science solutions, while O’Reilly emphasizes data-analytic thinking and business problems.
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