Posts Tagged ‘ Analytics-business interaction ’

Data sleaze: Uber and beyond

April 26, 2017
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There has been a barrage of negative publicity related to Uber recently. The latest salvo is a long article in the New York Times (link). This piece focuses on Uber's CEO, who was trained as a computer engineer, but my interest lies primarily in several revelations about how Uber collects and uses customer data. The key episode picked up by various outlets (e.g. TechCrunch, Wired) involves Uber "secretly identifying and…

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My pre-existing United boycott, and some musing on randomness and fairness

April 12, 2017
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You probably already saw the video - if not, do yourself a favor, and search for "man forcibly removed from overbooked United flight." Other than the video evidence, which is damning, we don't have many facts, other than assertions made by various parties, repeated endlessly on social media and mainline media. Some facts, such as the United CEO claiming the passenger was "belligerent," is an assault on the meaning of…

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What is Mr. Pruitt saying?

April 3, 2017
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In his latest provocation, the EPA Chief, Scott Pruitt, aborted an on-going process by his agency to ban a widely-used but potentially harmful pesticide known as chlorpyrifos (link to New York Times article). In my previous blog on his climate-change statement, I pointed out that people who attack data-driven conclusions for its "imprecision" will ignore any uncertainty if they want something to happen: However, when it comes to such decisions…

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Confused by machines, or spooked by the machine-makers

March 29, 2017
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This New York Times article draws attention to real trends in the financial investments industry but gets completely lost in the smoke around those pushing "machines" and "data". The trend most concerning to the investments industry is the sustained, large-scale outflow of money from "actively-managed" funds, mutual funds being the biggest category of such. The industry makes loads of money from management fees by promoting the idea that investors are…

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Collaboration with New York Public Library

March 27, 2017
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For many years now, the field of Data Science and Business Analytics has been booming, and hiring managers are finding a severe dearth of high-quality job-seekers. Meanwhile, there are a good number of people interested in entering the field but keep bumping into walls. Hiring managers like to hire experienced people for a host of reasons, including the fear of other hiring managers poaching their trained employees. For a number…

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One statement employing two resistance tactics to fend off the data

March 10, 2017
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I want to parse this statement by new EPA Chief Scott Pruitt, as quoted in this New York Times article: I think that measuring with precision human activity on the climate is something very challenging to do and there’s tremendous disagreement about the degree of impact, so no, I would not agree that it’s a primary contributor to the global warming that we see. I'm not going to talk about…

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Reading Everything is Obvious by Duncan Watts

February 15, 2017
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Reading Everything is Obvious by Duncan Watts

In his book, Everything is Obvious (Once You Know the Answer): Why Common Sense Fails, Duncan Watts, a professor of sociology at Columbia, imparts urgent lessons that are as relevant to his students as to self-proclaimed data scientists. It takes only nominal effort to generate narrative structures that retrace the past, Watts contends, but developing lasting theory that produces valid predictions requires much more effort than common sense. Watts’s is…

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Deep thinking about your data

February 3, 2017
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Deep thinking about your data

In the on-going series of posts about the IMDB dataset, from Kaggle, I have so far looked at several of the scraped variables, including the number of faces on movie posters (1, 2), plot keywords (3), and movie rating by title year (4). In this post, I tackle the variables resulting from a data merge between IMDB and Facebook. These columns have names like "Director Facebook Likes", "Actor 1 Facebook…

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Pre-processing data is not just about correcting errors

January 30, 2017
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Pre-processing data is not just about correcting errors

Exploration of IMDB rating data, by Kaiser Fung, founder of Principal Analytics Prep

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Numbersense and government accountability in the new political reality

January 24, 2017
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You've heard me say often, numbersense is the most important quality for good data analysts; little did I know that numbersense would become the new requirement for healthy American democracy. From the first day in office, the new President is at war with numbers (over attendance figures at his inauguration). But I believe that getting to the bottom of data-driven claims is a bi-partisan issue: while it is obvious that…

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