10 years on, same old same old

/ nsaunders

September 2, 2002

So what new skills will postdocs need to ensure that they don’t become science relics? The answer is math, statistics, and knowledge of a scripting language for computers.

— ­The Scientist, “Bioinformatics Knowledge Vital to Careers.” 16(17): 53.

February 8 2012

But two other skills are increasingly necessary: expertise in computer-programming languages designed to aid manipulation of large data sets, such as R, Perl or Python, and the ability to use these languages to analyse large amounts of data quickly.

— Nature, “Biostatistics: Revealing analysis.” 482: 263–265.

9 thoughts on “10 years on, same old same old”

  1. I think the key is Statistics. Knowledge of statistical analysis will enhance every postgrad professional.

  2. ummm … I think if they think Perl and Python are sufficient for manipulating large data sets by themselves, people are in for a rude shock (depending on your definition of large)

  3. I think one thing that’s different from 10 years ago is that back then, nearly all bench types were frightened of the UNIX command line (sometimes even of PAUP on a Macintosh or running BLAST from the NCBI website!). These days, while there are still technophobes, there is a new breed of bench scientist, one that isn’t really a bioinformatican, but one who can at least write simple scripts to reformat data and navigate around the command line to run existing pipelines.

  4. I think these days there are a lot more scientists that are using some scripting, stats and re-using existing data to enhance their own bio experimental results. I know a couple of great example of this. There is also such a huge competition for jobs that they are getting competitive advantages so its going to keep trending up. I have been teaching a PhD course for biomedical students and I try to pass on this message that you need these skills to be competitive but not all of them (want to) believe me :).

  5. Thanks for the comments – keep them coming! I posted this one because I had a strong reaction to it, but I can’t figure out exactly why it “set me off.” So I find the reactions of others very interesting.

  6. I’ve come into closer contact with bench-oriented folks again over the last year or so and I’m rather saddened at the lack of quantitative insight or instinct. People who measure things – complex things, in big machines that go “whoomph” a lot and have kinda slick interfaces that spew numbers – rarely seem to care about little things like reproducibility, measurement error, power or experimental design.

    Make an observation on a sample? Good, now do a second one and it’s a wrap. Paper writes itself!

    Ok, ok, I’m going back to my nursing home. But in my day, biochemists knew an integral from a differential. They did kinetics for pity’s sake!

  7. Belatedly, but having sat in on a few of these conversations, the pool of “essential” skills tends to blossom out ferociously – UI design, ontologies, functional testing, chemistry! – perhaps in line with the widediversity of things that pass for bioinformatics. I’ve decided there are only 3 essential core skills – scripting / programming, statistics, biology (of the domain you are investigating). Any less, and you’re “just” a programmer or following instructions. Everything else is optional, depending on your field.

    • I’d rank them differently: 40% statistics, 40% biology, 20% scripting. My justification is that knowing what to measure and how to do it is crucial. Tool use is then /relatively/ easy to acquire. All the scripting + biology in the world don’t give you any insight into the robustness of results (or teasing out signal from noise); conversely, all the statistics + scripting in the world don’t guarantee you’re asking a sensible question.

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