The Question to Ask Before Hiring a Data Scientist - Michael Li by Michael Li | 10:00 AM August 6, 2014 When hiring data scientists, there’s nothing more frustrating than making the wrong hire. Data scientists are in notoriously high demand, hard to attract, and command large salaries — compounding the cost of a mistake. At The Data Incubator, we’ve talked to dozens of employers looking to hire data scientists from our training program, from large corporates like Pfizer and JPMorgan Chase to smaller tech startups like Foursquare and Upstart. Employers that didn’t have good hiring experiences in the past often failed to ask a key question: Is your data scientist producing analytics for machines or humans? This distinction is important across organizations, industries, and job titles (our fellows are being placed at jobs with titles that range from Quant to Data Scientist to Analyst to Statistician). While this isn’t the only distinction among data scientists, it’s one of the biggest when it comes to hiring.
HISTORIA | BIG HISTORY | La Serie Esta nueva serie relaciona hechos significativos de la Historia con nuestra vida diaria desde una perspectiva científica. Un ejemplo: ¿sabías que el legado del ‘Titanic’ lo llevamos a diario en nuestros bolsillos? Cada vez que llamamos con el teléfono móvil utilizamos ondas de radio que se implantaron a raíz de que fracasaran las peticiones de rescate del emblemático transatlántico a través del telégrafo. También este aparato está relacionado con el Big Bang, puesto que aquella gran explosión dio origen al tantalio, un elemento escaso y misterioso empleado en pequeñísimas cantidades en la fabricación de los teléfonos móviles. Sin él, estos dispositivos serían 12 veces más grandes. Cada capítulo de la serie está contado de una forma totalmente novedosa, derribando los muros que separan la ciencia de la Historia y convirtiendo incluso a la Gran Historia en una disciplina emergente en las universidades de Estados Unidos.
How to Win At Rock, Paper, Scissors With Science A study of common strategies playing Rock, Paper, Scissors has provided advice on the best way to win, at least as long as your opponent has not read the same study. Rock, Paper, Scissors might once have been a game for children, but these days there are leagues for serious money and even a “world championship”. Meanwhile male lizards have been discovered to be playing the same game. If everyone was random in the way they played the game it would simply be a matter of chance who won. While people will throw away fortunes on games of chance, one with only three options would probably not hold attention. However, humans are not random number generating machines. There are already advice pages on the web for beating common strategies, but it is unclear how reliable their recommendations are, other than those that are simply logic. Psychologically this is hardly surprising, at least for new players.
Math 101: A reading list for lifelong learners Ready to level up your working knowledge of math? Here’s what to read now — and next. Math 101, with Jennifer Ouellette First, start with these 5 books… 1. “First published in 1930, this classic text traces the evolution of the concept of a number in clear, accessible prose. 2. “This bestselling book originally published in 1988 remains one of the best introductions to the basics of large numbers, statistics and probabilities with illustrations drawn from everyday life: sports, the stock market, the lottery and dubious medical claims, to name a few.” 3. “Pair Paulos with the just-released How Not to Be Wrong. 4. “Most of us take zero for granted, but there was a time when it simply didn’t exist, until some enterprising Babylonian soul inserted it as a placeholder in Eastern counting methods. 5. “The prose gets a bit turgid at times, and some readers might be deterred by the proofs and equations scattered throughout, but Berlinski has some lovely descriptions and turns of phrases. 1. 2. 3.
The Limits of Big Data: A Review of Social Physics by Alex Pentland In 1969, Playboy published a long, freewheeling interview with Marshall McLuhan in which the media theorist and sixties icon sketched a portrait of the future that was at once seductive and repellent. Noting the ability of digital computers to analyze data and communicate messages, he predicted that the machines eventually would be deployed to fine-tune society’s workings. “The computer can be used to direct a network of global thermostats to pattern life in ways that will optimize human awareness,” he said. The interview appeared when computers were used mainly for arcane scientific and industrial number-crunching. One of big data’s keenest advocates is Alex “Sandy” Pentland, a data scientist who, as the director of MIT’s Human Dynamics Laboratory, has long used computers to study the behavior of businesses and other organizations. Pentland’s idea of a “data-driven society” is problematic. Deciphering people’s behavior is only the first step.
5 charlas de TED que recomiendo (incluye ñapa) | Juan Fernando Zuluaga Esto lo publiqué hace 2 años 4 meses 14 días, por lo que sus referencias pueden estar desactualizadas. Si lo están, le ruego me lo haga saber en los comentarios para hacer la corrección. Si no, ¡que lo disfrute! Desde hace varios meses me he puesto como meta ver al menos una presentación de TED al día. Pues bien, desde que Netflix incluyó en sus contenidos audiovisuales todas las colecciones de TED, hay días en que veo 5 y 6 charlas de estas (aunque no lo recomiendo… a veces uno termina saturado y no puede procesar tanta información). Por eso he creado un listado en YouTube donde iré añadiendo las charlas que me parezcan interesantes… por ahora, añadí estas cinco que me repetí en los últimos días y creo muy relevantes: Y la ñapa: Sir Ken Robinson – ¿las escuelas matan la creatividad? Enlace corto:
50 Things Every Man Should Do: The Ultimate Bucket List Here’s a whole bunch of things to add to your to-do list. Whether it’s jumping out of a moving airplane or writing hand-written letters to those who’ve made a major impact on your life, most gents will hopefully get inspired by a few of these… – Start a business at some point, even if it’s just a side gig. – Learn to fly an airplane. – Coach a youth sport. – Own your dream car, pay cash. – Buy a plane ticket on the same day it takes off. – Buy a nice watch, pass it down to your son when he turns 18. – Go to the Super Bowl once. – Go to the World Series once. – Go to the World Cup once. – Learn a martial art. – Visit every continent, Antarctica optional. – Get a suit custom made for you. – Join the mile high club. – Write hand-written notes to those who’ve impacted your life in serious ways. – Improve your diet and fitness, get a 6 pack. – Learn to play an instrument decently well. – Learn the ins-and-outs of wine, actually know what you’re talking about. – Play golf at a legendary course.
4 reasons we should fix economic inequality It’s safe to say that economic inequality bothers us. But why? Harvard philosopher T. M. The great inequality of income and wealth in the world, and within the United States, is deeply troubling. One obvious reason for redistributing resources from the rich to the poor is simply that this is a way of making the poor better off. A justification for reducing inequality through non-voluntary means, such as taxation, needs to explain why redistribution of this kind is not just robbery. These reasons for redistribution are strongest when the poor are very badly off, as in the cases Singer describes. It’s important to note, though, that there is another sense in which these reasons are not egalitarian: They are, fundamentally, reasons to increase the well-being of the poor rather than objections to inequality, that is to say, objections to the difference between what some have and what others have. 1. 2. 3. None of these objections is an expression of mere envy. 4. T.
The Mathematical Shape of Big Science Data Simon DeDeo, a research fellow in applied mathematics and complex systems at the Santa Fe Institute, had a problem. He was collaborating on a new project analyzing 300 years’ worth of data from the archives of London’s Old Bailey, the central criminal court of England and Wales. Granted, there was clean data in the usual straightforward Excel spreadsheet format, including such variables as indictment, verdict, and sentence for each case. But there were also full court transcripts, containing some 10 million words recorded during just under 200,000 trials. Today’s big data is noisy, unstructured, and dynamic. “How the hell do you analyze that data?” “In physics, you typically have one kind of data and you know the system really well,” said DeDeo. DeDeo is not the only researcher grapping with these challenges. Peter DaSilva for Quanta Magazine Gunnar Carlsson, a mathematician at Stanford University, uses topological data analysis to find structure in complex, unstructured data sets. Ayasdi