New data analysis competitions


  • Hiding in plain sight -- political profiling of voters.

    What is clear is that there are a range of companies whose business models are to exploit the data people share online in such a way that intimate personal details about a person’s beliefs, habits, and behavior can be better understood and used for the purpose of allowing political parties to target these individuals with political messages.

    Just this week I started reading Hacking the electorate see here, which is quite related to this topic.


  • One of the podcasts I regularly listen to, Letters and politics, interviewed George Zarkadakis last week.

  • AI Designers Find Inspiration in Rat Brains.

    The five-year program, funded to the tune of US $100 million by the Intelligence Advanced Research Projects Agency (IARPA), keeps a tight focus on the visual cortex, the part of the brain where much visual-information processing occurs. Working with mice and rats, three Microns teams aim to map the layout of neurons inside 1 cubic millimeter of brain tissue. That may not sound like much, but that tiny cube contains about 50,000 neurons connected to one another at about 500 million junctures called synapses. The researchers hope that a clear view of all those connections will allow them to discover the neural "circuits" that are activated when the visual cortex is hard at work.

  • A list of data science podcasts.

  • J.P.Morgan's massive guide to machine learning and big data jobs in finance. The article summarizes the report but I haven't been able to find the original. If anyone knows of any link to the source PDF and can e-mail it to me, I'd be grateful.

    Titled, 'Big Data and AI Strategies' and subheaded, 'Machine Learning and Alternative Data Approach to Investing', the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don't they'll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

Data Links is a periodic blog post published on Sundays (specific time may vary) which contains interesting links about data science, machine learning and related topics. You can subscribe to it using the general blog RSS feed or this one, which only contains these articles, if you are not interested in other things I might publish.

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