Difference between DataScience Vs Biostatistician

Rathinavel M.S
Nov 5 · 2 min read

Similarities:

· Knowledge of statistics is a strong foundation for both.

· Both deal with causal inference problems with observational and experimental data.

· Data is highly structured, with lots of potential for confounding and external noise.

· Outcome metrics must be carefully designed.

· There are methodological similarities — clinical trial design concepts are closely related to methods for UI experiments (power calculations, randomization, mid-experiment checks to verify that not too much harm is being done, etc).

· Epidemiological models and social network analysis also have some overlap.

Differences:

· Obviously, there are differences in domain-specific knowledge and models. If you’re working on a genetic study, you need to know a bit about DNA and inheritance.

· Data science is deals with respect of any field of structured/unstructured/ semi-structured data while biostatistics deals only with bio-related data with limitations.

· Biostatistics prefers straightforward statistical analyses and isn’t terribly fond of machine learning (although bioinformatics, a related field, is). Feature selection is usually driven by expertise rather than data.

· Biology data is inherently expensive, whereas data-science data is relatively cheap, given certain startup costs. This affects experimental design decisions.

· Data scientists tend to use more general-purpose programming languages (R vs SAS vs Python), while biostatisticians usually stick with SPSS vs STATA vs EpiInfo.

After working as a data scientist for about two year, data science (and industry work in general) moves much faster than biostatistics research. You don’t spend months trying to get an extra .01 increase in AUC — you invest a reasonable amount of resources, and then you accept the conclusions and move on to the next thing.

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