The Big Data Brain Drain: Why Science is in Trouble. Regardless of what you might think of the ubiquity of the "Big Data" meme, it's clear that the growing size of datasets is changing the way we approach the world around us.
This is true in fields from industry to government to media to academia and virtually everywhere in between. Our increasing abilities to gather, process, visualize, and learn from large datasets is helping to push the boundaries of our knowledge. But where scientific research is concerned, this recently accelerated shift to data-centric science has a dark side, which boils down to this: the skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree.
L’histoire de l’innovation contemporaine c’est les Big Data. La lecture de la semaine provient de la vénérable revue The Atlantic et on la doit à Erik Brynjolfsson, économiste à la Sloan School of Management et responsable du groupe Productivité numérique au Centre sur le Business numérique du Massachusetts Institute of Technology et Andrew McAfee auteurs Race Against the Machine (“La course contre les machines où comment la révolution numérique accélère l’innovation, conduit la productivité et irréversiblement transforme l’emploi et l’économie”).
The Human Face of Big Data. Big Data, Big Hype: Big Deal. ‘Big data’ is dead. What’s next? How can big data and smart analytics tools ignite growth for your company?
Find out at DataBeat, May 19-20 in San Francisco, from top data scientists, analysts, investors, and entrepreneurs. Register now and save $200! Big data is dead, long live big data: Thoughts heading to Strata. A recent VentureBeat article argues that “Big Data” is dead.
It’s been killed by marketers. That’s an understandable frustration (and a little ironic to read about it in that particular venue). As I said sarcastically the other day, “Put your Big Data in the Cloud with a Hadoop.” You don’t have to read much industry news to get the sense that “big data” is sliding into the trough of Gartner’s hype curve. That’s natural. Research paper: What big data can do for the cultural sector « Cross Innovation. These days, data sizes are almost infinite, and organizations learn a lot about their position and succesful strategies by simply analyzing data.
However, most cultural industries have not yet implemented this concept. Anthony Lilley and Paul Moore give their views on how data can benefit creative industries. This paper argues the value of big data analysis for creative institutions, but also that most of them are not even taking online data into account. The paper is a collaboration between Paul Moore, professor at the University of Ulster and researcher on (theory and practice of) the creative industries, and Anthony Lilley, media practitioner and creative concept developer with an international experience in the creative industries. Dr. Brian Lowe, SUNY Oneonta – Analyzing "Big Data"
In today’s Academic Minute, Dr.
Brian Lowe of the State University of New York Oneonta explains why "Big Data" is becoming a focus of academic inquiry. Dr. Brian Lowe, SUNY Oneonta – Analyzing Big Data Brian Lowe is an associate professor of sociology at the State University of New York Oneonta where his research and teaching interests include sociological theories, animal and society, cultural and comparative-historical sociology and spectacular conflicts.
His work has appeared in a number of peer-reviewed journals and in 2006 he published, Emerging Moral Vocabularies: The Creation and Establishment of New Forms of Moral and Ethical Meanings. Dr. Where do tweets go? What these conversations often fail to address is that many other organizations—from political groups to corporations to nonprofits—are also watching us. Why you should never trust a data visualisation. First of all, let me be clear: the headline of this article is a reference to Pete Warden's post, and should be read in the same way - as a caution against blind acceptance, rather than the wholesale condemnation of data visualisation.
An excellent blogpost has been receiving a lot of attention over the last week. Pete Warden, an experienced data scientist and author for O'Reilly on all things data, writes: The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries ... I thought I was publishing an entertaining view of some data I'd extracted, but it was treated like a scientific study.
This is an important acknowledgement of a very real problem, but in my view Warden has the wrong target in his crosshairs. Ethique Big Data. Le Big Data : c’est de « la connerie » Directeur technologique de la campagne 2012 de Barack Obama, Harper Reed a son mot à dire sur le thème du Big Data.
Et ce ne sont pas des éloges. En tout cas en ce qui concerne l’utilisation de ce terme par l’industrie IT. L’emploi à l’épreuve des algorithmes. Par Hubert Guillaud le 03/05/13 | 6 commentaires | 4,691 lectures | Impression.
Big Data : nouvelle étape de l’informatisation du monde. To Hypothesize or Not to Hypothesize. Forget big data, small data is the real revolution. There is a lot of talk about "big data" at the moment.
For example, this is Big Data Week, which will see events about big data in dozens of cities around the world. But the discussions around big data miss a much bigger and more important picture: the real opportunity is not big data, but small data. Not centralized "big iron", but decentralized data wrangling. Not "one ring to rule them all" but "small pieces loosely joined". Universities Offer Courses in a Hot New Field - Data Science. L'Institut pour la Science des Données participe à l'année internationale de la statistique. Data Jujitsu: The art of turning data into product. Having worked in academia, government and industry, I’ve had a unique opportunity to build products in each sector.
Much of this product development has been around building data products. Just as methods for general product development have steadily improved, so have the ideas for developing data products. Thanks to large investments in the general area of data science, many major innovations (e.g., Hadoop, Voldemort, Cassandra, HBase, Pig, Hive, etc.) have made data products easier to build.
Les 3 V du Big Data : Volume, Vitesse et Variété. Le volume des données explose. A Data Scientist's Real Job: Storytelling - Jeff Bladt and Bob Filbin. Every morning at DoSomething.org, our computers greet us with a report containing over 350 million data points tracking our organization’s performance. Our challenge as data scientists is to translate this haystack of information into guidance for staff so they can make smart decisions — whether it’s choosing the right headline for today’s email blast (should we ask our members to “take action now” or “learn more”?) Or determining the purpose of our summer volunteer campaign (food donation drive or recycling campaign?). In short, we’re tasked with transforming data into directives. Good analysis parses numerical outputs into an understanding of the organization. We “humanize” the data by turning raw numbers into a story about our performance. Is Big Data Dying? Has big data been the victim of too much hype?
Has it failed to live up to its promise? Or have businesses asked too much of big data and not given enough? The newsonomics of a news company of the future. What will news companies look like in 2018? How will they operate differently? That future is coming into focus. While many publishers’ vision is still quite blurry, it’s the Financial Times that is clearest-eyed about its roadmap and its future. The FT’s clarity first struck me when I sat down for an introductory talk with FT.com managing director Rob Grimshaw in London in fall 2009. His office, just off Southwark Bridge, offered a view of the Thames that forced you to think about the long history of newspapering in that city, a business then in a deep, deep recession along with the rest of the global economy. Non, les données ne sont pas du pétrole... Il ne se passe plus une semaine sans un dossier spécial titrant sur "les data, pétrole du XXIe Siècle", "data is the new oil", "les données, le nouvel or noir", "vos données personnelles valent 315 milliards d'euros", "profitez des opportunités des big data", voire même un "trésor caché" et j'en passe.
On comprend bien la métaphore : les données personnelles, les données publiques, les données de l'internet des objets seraient comme le pétrole : une ressource naturelle, fluide, susceptibles de toutes sortes de transformations, et porteuses d'un énorme potentiel de valeur. Plus encore, elles seraient le ferment d'une nouvelle révolution industrielle, appelées à plier l'économie mondiale à leur puissant potentiel industriel. On comprend la métaphore, mais elle n'en n'est pas moins lassante. Competitions. Why becoming a data scientist is NOT actually easier than you think - josephmisiti's posterous. Software engineer’s guide to getting started with data science. Introduction to Data Science. About the Course Commerce and research are being transformed by data-driven discovery and prediction.
Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression). Course Syllabus. Career Advice: How do I become a data scientist. » Getting Started with Data Science hilarymason.
So you want to be a data scientist? : Nature Jobs Blog. Data scientist has been billed as the ‘sexiest job of the 21st Century’ – but who are data scientists, and how can you get in on the action? Guest post by Michael Koploy of SoftwareAdvice.com By now, pretty much everyone has heard that “Big Data” will be the next “big thing” to revolutionise how we work, live and communicate. But who will manage the Walmart database that contains over 2.5 petabytes of data from the retailer’s 1 million customer transactions per hour? Big Data. Big Data. Peasant Muse: From Data Self to Data Serf.
"We belong to you, but the land belongs to us. " - Russian Peasant Refrain "But even when I am at a loss to define the essence of freedom I know full well the meaning of captivity. " - Adam Zagajewski, 'Freedom' "You can't be what you were So you better start being just what you are You can't be what you were Time is now and it's running out…" - Fugazi, 'Bad Mouth' A few months ago, in preparation for my (then) upcoming 'Theorizing the Web' presentation, I tweeted the peasant refrain quoted above and made a comment to the effect that this is how, I believe, many feel when it comes to social media platforms and the data those platforms contain.
From my perspective, the heart of data serfdom centers on a question over what counts as certified, verifiable knowledge and the degree to which that knowledge is permitted to circulate or be modified. This in itself would not be revelatory. In Social Graph vs. The Promise and Peril of the 'Data-Driven Society' Left: Damian Dovarganes/Associated Press; Jim Hollander/European Pressphoto AgencyData from credit card purchases and cell phone use provide a wealth of information about personal behavior. A small group of academics, business executives and journalists gathered at the M.I.T. Media Lab last Thursday, and the purpose was to toss out ideas and discuss the concept of “Data-Driven Societies.” A daunting topic, ambitious and vague at once, it seems.
#2 Jeff Hammerbacher, Chief Scientist, Cloudera and DJ Patil, Entrepreneur-in-Residence, Greylock Ventures - Tim O’Reilly: The World’s 7 Most Powerful Data Scientists. Data Science Blogs. The #1 Career Mistake Capable People Make. Volume 1, Issue 1. Learning To Be A Data Scientist. Columbia University to Create Data Sciences Institute in NYC. Columbia University will start an Institute for Data Sciences and Engineering in New York City. The program, which will be housed at the school’s existing campuses in Morningside Heights and Washington Heights, is expected to employ 75 new faculty members over the next 15 years, according to a news release from the city announcing the plan.
The agreement includes the creation of 44,000 square feet of applied-science and engineering space by 2016. New York City will provide $15 million in financial assistance, including discounted-energy transmission costs and partial debt forgiveness, according to the release. Mayor Michael Bloomberg has made developing the city’s technology industry a major initiative. Cornell University in Ithaca, New York, and its partner Technion-Israel Institute of Technology, last year won a city contest to build an engineering campus with a grant of land on Roosevelt Island and $100 million for infrastructure improvements. New York University wants to train the next generation of data scientists. Everybody and their mother is on the hunt for a data scientist, dubbed the “sexiest job of the 21st century,” by the Harvard Business Review. To train the next generation, New York University (NYU) has launched a data science and statistics initiative, which is intended to drive breakthrough research in other programs, including medicine and technology.
The new website for the program cites research from McKinsey that projects the U.S. alone will need 140,000 to 190,000 people with deep analytic skills by 2018 as well as 1.5 million managers and analysts for “big data” jobs. How to think and talk like a collaborative data scientist. The buzz about analyzing "big data" means someone has to do the analysis. Are You a Data Whisperer? The curse of big data. Data Scientists: The Definition of Sexy. Harvard Business Review: Data Scientist Is The 'Sexiest Job Of The 21st Century' Data Scientist: The Sexiest Job of the 21st Century. Microsoft: Big Data Dominated By Marketing, Sales.
Culture & Big Data: Four Essential Questions. Big Data Is Great, but Don’t Forget Intuition. Plongée au cœur du Web. Big Data, grande illusion. Vertigineux "big data". Big Data’s Big Problem: Little Talent - Tech Europe. Specials : Nature.
Keep Your Data Scientist…Send Me A Data Artist! — International Institute for Analytics. Internet, Politics, Policy 2012: Big Data, Big Challenges? International Journal of Internet Science, Volume 7, Issue 1. RESEARCH CENTER FOR DATAOLOGY AND DATASCIENCE. Search Results » burrell. Le Big Data en analyse : qu’est ce que le Big Data ? (1/5) « Rendez-vous à Vheissu. Top 5 Myths About Big Data. A Very Short History of Data Science. A Taxonomy of Data Science. Big Data’s Impact in the World. A data science cheat sheet. Big Data in 2012: Hadoop, Big Data Apps, Data Science Tools, Cloud Collision and More. How Big Data Gets Real. Why the term "data science" is flawed but useful. Data science is a pipeline between academic disciplines.
Data Science: a literature review. What is "Data Science" Anyway? Six months after "What is data science?" What is Data Science. Data Science Central. What is Data Science and Why Should I Care? « The Slalom Blog. What is a data scientist? The Evolution of "What is Data Science?" Rise of the Data Scientist. Hal Varian on how the Web challenges managers - McKinsey Quarterly - Strategy - Innovation. The Three Sexy Skills of Data Geeks « Dataspora. For Today’s Graduate, Just One Word - Statistics. (9) What is data science. What is data science?