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Data science tips and tricks from the developer community

blog.algorithmia.com

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Re: Data science tips and tricks from the developer community

#41

Post by joe_the_user »

arcanus wrote: post #10
minimaxir wrote: post #2

It's worth nothing that the article does not discuss the reproducibility of results (e.g. with a Jupyter Notebook) and the implementation of said results (e.g. deploying/validating models), both of which matter much more than any code style conventions for data-related projects.

^ this. I cannot describe how many times I've been shown results and when asking how to reproduce them, after several notes (and sometimes complaints to higher ups) I eventually get a series of command line arguments or a barely functioning R-script. These conclusions are too important to be so sloppily produced. We need verification, validation and uncertainty quantification for any result provided to decision maker…

To put it cynically, if you other people can reproduce your results, you might not be demonstrating that you are x10 more productive than them.

Which is to say I think starting with the idea that you're aim for x10 is pernicious and tends to create dysfunctional teams. The claim some developers in some circumstances are ten time more productive than others may or may not be true but software development needs processes whose goal is to help an entire team rather than helping an individual to that "level".

Re: Data science tips and tricks from the developer community

#42

Post by cheez »

joe_the_user wrote: post #41
arcanus wrote: post #10

Earlier quoted context omitted.

^ this. I cannot describe how many times I've been shown results and when asking how to reproduce them, after several notes (and sometimes complaints to higher ups) I eventually get a series of command line arguments or a barely functioning R-script. These conclusions are too important to be so sloppily produced. We need verification, validation and uncertainty quantification for any result provided to decision maker…

To put it cynically, if you other people can reproduce your results, you might not be demonstrating that you are x10 more productive than them. Which is to say I think starting with the idea that you're aim for x10 is pernicious and tends to create dysfunctional teams. The claim some developers in some circumstances are ten time more productive than others may or may not be true but software development needs process…

This is why the only time I use 10x is "your team will become 10x more productive."

Re: Data science tips and tricks from the developer community

#43

Post by webnrrd2k »

joe_the_user wrote: post #41
arcanus wrote: post #10

Earlier quoted context omitted.

^ this. I cannot describe how many times I've been shown results and when asking how to reproduce them, after several notes (and sometimes complaints to higher ups) I eventually get a series of command line arguments or a barely functioning R-script. These conclusions are too important to be so sloppily produced. We need verification, validation and uncertainty quantification for any result provided to decision maker…

To put it cynically, if you other people can reproduce your results, you might not be demonstrating that you are x10 more productive than them. Which is to say I think starting with the idea that you're aim for x10 is pernicious and tends to create dysfunctional teams. The claim some developers in some circumstances are ten time more productive than others may or may not be true but software development needs process…

Yes, it's better to have an x10 team, rather than an x10 developer.

Developers should strive to better themselves, but it's important not to fool yourself, too. Having a strong team is almost always better from a business point of view.

Re: Data science tips and tricks from the developer community

#44

Post by mikeyanderson »

mattmanser wrote: post #19

A 10x developer is not ten times more productive than the average developer, they're 10x more productive than the worst developer. Wish this myth would stop perpetuating, they're very clear in the original study. Here's some more details from the horses mouth, Steve McConnell who popularized the concept in Code Complete: http://www.construx.com/10x_Software_Development/Origins_of_...

Sure from a "task" stand point, but take quality, reusability, unique approaches, business sense, etc. and the best devs add easily 10x value if not more. Right?

Re: Data science tips and tricks from the developer community

#45

Post by kolbe »

tw1010 wrote: post #16

Funny that learning actual mathematics isn't mentioned.

The whole time I was sarcastically thinking, "yeah I'm sure you get 10x by choosing consistent naming conventions. That will make up for the months of tearing your hair out trying to learn how ANNs work without a very solid understanding of math/stats."

Re: Data science tips and tricks from the developer community

#46

Post by jupiter90000 »

In my opinion, this article would make more sense if two things were first defined: 1) What is a data scientist? 2) What would it mean for someone defined in 1 above to be 10x more productive?

Then it says nothing or little about understanding foundations of research, math, and computer science, instead going into superficial things like 'understand the business' and code examples that could be produced by a beginner level programmer.

This is not how to get to 10x, more like barely, possibly competent.

Re: Data science tips and tricks from the developer community

#47

Post by mikeyanderson »

mooneater wrote: post #39

Most of this nit-picky advice will not increase my leverage. What I think would: occasional access to domain experts in specific niches of data science.

What would that look like? Data-scientist AMAs? Or in person? What would you like to see?

Re: Data science tips and tricks from the developer community

#48

Post by tnecniv »

gilbetron wrote: post #15
Angostura wrote: post #13

Earlier quoted context omitted.

Potentially problematic for those who want to check your findings in 30 years time?

Is there a good solution to that problem, though? (Serious question). I recently did a laptop refresh and am using it as an opportunity to solidify my approach to ML development, and would love to hear if there is a good solution to long-term reproducibility. I'm currently leaning towards Docker, but maybe Vagrant or another "pure" VM approach is better...

Well you can always fire up LaTeX and write a report. If detailed enough, that in conjunction with the data set should be enough to survive anything.

Re: Data science tips and tricks from the developer community

#49

Post by gipp »

NumberCruncher wrote: post #35
minimaxir wrote: post #2

It's worth nothing that the article does not discuss the reproducibility of results (e.g. with a Jupyter Notebook) and the implementation of said results (e.g. deploying/validating models), both of which matter much more than any code style conventions for data-related projects.

In my experience if you develop a "data science pipeline" forcing the data scientist to build - reproducible - validated - back-tested - easy to deploy models, they are going to hate it. It just kills the fun and/or makes obvious if they made a mistake.

So we should sacrifice all the things that actually make a Data Scientist's work valuable in the name of fun and obscuring mistakes?

Fun I almost get, obviously good for productivity (though I think you'd really be sacrificing productive output for non-productive output), but I just don't get where you're even coming from with the "making mistakes more obvious" angle.

Re: Data science tips and tricks from the developer community

#50

Post by gipp »

kolbe wrote: post #45
tw1010 wrote: post #16

Funny that learning actual mathematics isn't mentioned.

The whole time I was sarcastically thinking, "yeah I'm sure you get 10x by choosing consistent naming conventions. That will make up for the months of tearing your hair out trying to learn how ANNs work without a very solid understanding of math/stats."

90% of data scientists do not use neural networks. Of those who do, 90% shouldn't be, and are letting what's fun/interesting get in the way of actually producing value.

The fact of the matter is that if you're not FB/GOOG/AMAZ, the vast majority of what companies need from their data scientists actually requires very little advanced mathematics, and much more focus on rigor, reproducibility, and good deployment/engineering practices.

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