Domino Data Lab Blog

I do not have any specific recommendations, aside from this guide to start: https://blog.dominodatalab.com/creating-interactive-crime-maps-with-fo…

For a data science flavor, see https://blog.dominodatalab.com For web dev, Netlify posts frequently and even has podcasts: https://www.netlify.com/blog/

Full Disclosure, I am the Chief Data Scientist of Domino Data Lab (https://www.dominodatalab.com), a company that builds tools for collaboration/scaling of data science a quantitative research teams. I spoke about our view on data provenance at UseR! 2016 and if you're curious you can watch the video here https://blog.dominodatalab.com/providing-digital-provenance-modeling-p… The rest of this comment is not about reproducibility and provenance however, it's mostly about collaboration. I really like this write-up. It's really quite prescient for a blog post from 2013! There have been amazing advances in the tooling for individual data scientists. RStudio, Jupyter, and Zeppelin notebooks have provided a really fluid REPL for data science. This has really compressed the workflows he discusses, and provides feedback and direction to data scientists much more quickly than the program oriented process. It’s interesting to note how an unexpected direction (notebooks) can impact praxis. I refer to these two visualizations of the data science process often. The first is by Szilard Pafka, and it details the workflow he has experienced by having models in production for well over a decade at this point. It’s available here: https://raw.githubusercontent.com/szilard/datascience-1slide/master/ds… What I really like about this is that it really shows the centrality of model validation. From model validation, you can often find yourself having to go back to exploration,…

The submitted article was https://blog.dominodatalab.com/data-scientist-programmer-mutually-excl… with the title "Data Scientist? Programmer? Are They Mutually Exclusive?" Since it summarizes a talk, we changed the URL to that of the talk, in keeping with the HN guidelines' request for original sources (https://news.ycombinator.com/newsguidelines.html).

> Sure, and if you need to use another language from your Jupyter notebook, you can't. Not entirely true, BTW. https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/

I think that depends on what you mean; Jupyter lets you run magic[0] commands (run bash commands in subprocesses), and there's also a bash kernel[1]. [0]: https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/ [1]: https://github.com/takluyver/bash_kernel

They do model/feature storage and versioning just fine. Or at least Luigi does - I use it daily. And feature extraction and modelling is my main usecase. It works really well, across Spark, Scikit and R, saving data in HDFS. Some examples: https://blog.dominodatalab.com/luigi-pipelines-domino/ http://blog.richardweiss.org/2016/10/13/kaggle-with-luigi.html https://github.com/Atreya22/luigi_rosmann_sales

Interesting talk on the subject from a data scientist at Instacart: https://blog.dominodatalab.com/data-science-instacart/

Full disclosure: I work in marketing at Domino. Domino offers prebuilt deep learning environments on its data science platform. Tensorflow, PyCUDA, Theano, H2O, a variety of R deep learning packages, and many other tools are available. The environments run on Amazon's Nvidia GRID and Tesla instances. This post is a little old but will give you an idea as to what you can accomplish: http://blog.dominodatalab.com/gpu-computing-and-deep-learning/ We offer select prospects access to GPU compute when trialing Domino. Let me know if your team is interested.

I just googled it, I don't know a lot about Jupyter, but yes: http://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/ http://ipython.readthedocs.org/en/stable/interactive/magics.html#cell-…

One approach is to convert the code to use parallelism. For an example of how to do it in Python using joblib see this article: http://blog.dominodatalab.com/simple-parallelization/ Even if you can't afford a 32-core instance, you might get to use 4 cores in your laptop.

404. Correct URL: http://blog.dominodatalab.com/topic-modeling-in-sept-11-news-articles/