Blog archive, page 91

Data Science

R Notebooks in the Cloud

We recently added a feature to Domino that lets you spin up an interactive R session on any class of hardware you choose, with a single click, enabling more powerful interactive, exploratory work in R without any infrastructure or setup hassle. This post describes how and why we built our "R Notebook" feature.

By Nick Elprin5 min read

Perspective

Management lessons from software engineering

As I've evolved from being an individual software developer to managing teams and starting a company—a data science platform—I continue to notice parallels between the principles of good engineering and the principles of good management. I have no delusions of novelty here but it's an interesting topic with a lot of surface area, so I plan to write more about this over the coming months. For now, I want to focus on one specific similarity: situations where "someone" is unable to do what has been asked of them.

By Nick Elprin6 min read

Data Science

Reflections on bootstrapping

We recently hired a Head of Marketing, and one of the bigger challenges in doing so surprised me: many candidates expressed serious concern about our having bootstrapped the company rather than using the more traditional venture capital-backed approach. I thought our approach was better, faster, smarter – but that’s not what many candidates saw.

By Matthew Granade9 min read

Data Science

Crunchbase network analysis with Python

Crunchbase recently converted its backend database to a Neo4j graph database. This will give it great flexibility in the future, but for now, the data is exposed similarly to how it always has been: individual entities are retrieved and attribute data must be used to form edges between them prior to any graph analysis. Aside from traversing links manually on the web pages, there are no provisions for graph analysis.

By Casson Stallings6 min read

Data Science

Easy Parallel Loops in Python, R, Matlab and Octave

The Domino platform makes it trivial to run your analysis in the cloud on very powerful hardware (up to 32 cores and 250GB of memory), allowing massive performance increases through parallelism. In this post, we'll show you how to parallelize your code in a variety of languages to utilize multiple cores. This may sound intimidating, but Python, R, and Matlab have features that make it very simple.

By Nick Elprin3 min read

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