Category: Data Science
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11 Essential Features that Visual Analysis Tools Should Have
[A version of this post appears on the O’Reilly Strata blog.] After recently playing with SAS Visual Analytics, I’ve been thinking about tools for visual analysis. By visual analysis I mean the type of analysis most…
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Scalable streaming analytics using a single-server
[A version of this post appears on the O’Reilly Strata blog.] For many organizations real-time1 analytics entails complex event processing systems (CEP) or newer distributed stream processing frameworks like Storm, S4, or Spark Streaming. The latter…
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Simpler workflow tools enable the rapid deployment of models
[A version os this post appears on the O’Reilly Strata blog.] Data science often depends on data pipelines, that involve acquiring, transforming, and loading data. (If you’re fortunate most of the data you need is already…
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The re-emergence of Time-series
[A version of this post appeared on the O’Reilly Strata and Radar blogs.] My first job after leaving academia was as a quant1 for a hedge fund, where I performed (what are now referred to as)…
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Data Science tools: Are you “all in” or do you “mix and match”?
[A version of this post appears on the O’Reilly Strata blog.] An integrated data stack boosts productivity As I noted in my previous post, Python programmers willing to go “all in”, have Python tools to cover…
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Python data tools just keep getting better
[A version of this post appeared on the O’Reilly Strata blog.] Here are a few observations inspired by conversations I had during the just concluded PyData conference1. The Python data community is well-organized: Besides conferences (PyData,…
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Data Science Tools: Fast, easy to use, and scalable
[A version of this post appears on the O’Reilly Strata blog.] Here are a few observations based on conversations I had during the just concluded Strata Santa Clara conference. Spark is attracting attention I’ve written numerous…
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MLbase: Scalable Machine-learning made accessible
[Cross-posted on the O’Reilly Strata blog.] In the course of applying machine-learning against large data sets, data scientists face a few pain points. They need to tune and compare several suitable algorithms – a process that…
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2012 Revenue of some Big Data companies
The chart below is from Wikibon’s estimates1 of the 2012 revenue of some Big Data companies. Using d3 I drew a chart that shows 2012 revenue in millions, as well as the share of revenue derived…
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Mining Time-series with Trillions of Points: Dynamic Time Warping at scale
Take a similarity measure that’s already well-known to researchers who work with time-series, and devise an algorithm to compute it efficiently at scale. Suddenly intractable problems become tractable, and Big Data mining applications that use the…