Data Science Blog | Practical Techniques (8)

Reproducible Research in Computational Sciences

This guest post was written by Arnu Pretorius, a Masters student in Mathematical Statistics at the MIH Media Lab, Stellenbosch University. Arnu's...

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Model-Based Machine Learning and Probabilistic Programming in RStan

In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem,...

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An Introduction to Model-Based Machine Learning

This guest post was written by Daniel Emaasit, a Ph.D. Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's...

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Providing Digital Provenance: from Modeling through Production

At last week's useR! R User conference, I spoke on digital provenance, the importance of reproducible research, and how Domino has solved many of the...

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Announcing Enhanced Apache Spark Support

Domino now offers data scientists a simple, yet incredibly powerful way to conduct quantitative work using Apache Spark. Apache Spark has captured...

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Orchestrating Pipelines with Luigi and Domino

Building a data pipeline may sound like a daunting task. In this post, we will examine how you can use Luigi - a library specifically designed to...

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Ugly Little Bits of the Data Science Process

This morning there was a great conversation on Twitter, kicked off by Hadley Wickham, about one of the ugly little bits of the data science process.

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Moving Academic Researchers into a Corporate Data Science World

This webinar was recorded on May 25, 2016 There are big cultural differences between data science practices in academic and corporate settings. For...

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Better Knowledge Management for Data Science Teams

We’re excited to announce a set of big new features that make it easier for you to find and reuse past data science work in your team and...

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“Unit testing” for data science

An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software,...

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Making Data Science Fast: Survey of GPU Accelerated Tools

This talk took place at the Domino Data Science Pop-up in Austin, TX on April 13, 2016 In this talk, Mazhar Memon, CEO and Co-founder at...

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The R Data I/O Shootout

We pit newcomer R data I/O package, feather, against popular packages data.table, readr, and the venerable saveRDS/writeRDS functions from base R....

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How Machine Learning Amplifies Inequality in Society

This talk took place at the Domino Data Science Pop-up in Austin, TX on April 13, 2016 In this talk, Mike Williams, Research Engineer at Fast...

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Visualizing Machine Learning with Plotly and Domino

This post was contributed by Chelsea Douglas, a Software Engineer at Plotly. I recently had the chance to team up with Domino Data Lab to produce a...

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Uber and the Need for a Data Science Platform

For those wondering if data science platforms are really a thing, there’s a great article by Kevin Novak, the head of Uber’s Data Science Platform...

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The Real Value of Containers for Data Science

Every year, $50 of your taxes is invested in research that can't be reproduced.Erik Andrejko, VP Science, The Climate Corporation, speaking at...

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Data Exploration with Pandas Profiler and D-Tale