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Tony Searl

5 Predictions for Online Data In 2011 - 1 views

mashable.com/...data-predictions

data predictions 2011

shared by Tony Searl on 22 Dec 10 - No Cached
  • people have openly wondered whether the social media expert will go the way of the webmaster
  • data that is accessible and transportable and managed by its rightful owner — you
  • Backing up your data is just the first step, of course, a function that saves a seat for an entirely different function eventually.
  • ...4 more annotations...
  • When I am finally able to join my data from disparate services with a unified view and the right accompanying toolset, I’ll be able to do all kinds of derivation and detection
  • The tension between data that is sold or bestowed and data that is found or acquired is, for now, a productive dynamic.
  • We’ll see open data disrupt industries and verticals ranging from air travel to journalism to religion. We’ll see new kinds of museum displays, classrooms
  • Data knows everything we know, everything we don’t know, and, as it turns out, even a few things we don’t know we don’t know.
  • Tony Searl on 22 Dec 10
     
    true data science involves a heavy dose of machine learning, code skills, math chops and deep domain expertise.
Nigel Coutts

Tinkering with Old Technology - The Learner's Way - 0 views

thelearnersway.net/...tinkering-with-old-technology

technology education tools teaching collaboration learning

shared by Nigel Coutts on 19 Jun 16 - No Cached
  • Nigel Coutts on 19 Jun 16
     
    As technology evolves and its inner workings increasingly disappear from view, replaced with solid-state parts hidden by glass, aluminium and plastic, our understanding of what makes the world operate is similarly impeded. When machinery from just a few decades ago is viewed a world of moving parts, linkages, cogs and levers is revealed. These mechanical objects contain an inherent beauty and inspire curiosity in ways that modern devices with their pristine surfaces and simplified design language do not. Opportunities to explore devices from the past open our eyes and lead us to new questions of how our devices function, how machines do the jobs we need them to do and how engineers solve problems.
dean groom

'Teach Naked' Effort Strips Computers From Classrooms - Technology - The Chronicle of H... - 0 views

chronicle.com/...47398

powerpoint teaching education technology learning lectures

shared by dean groom on 05 Aug 09 - Cached
  • might need to stay a low-tech zone to survive.
  • dean groom on 05 Aug 09
     
    ollege leaders usually brag about their tech-filled "smart" classrooms, but a dean at Southern Methodist University is proudly removing computers from lecture halls. José A. Bowen, dean of the Meadows School of the Arts, has challenged his colleagues to "teach naked"-by which he means, sans machines. More than any thing else, Mr. Bowen wants to discourage professors from using PowerPoint, because they often lean on the slide-display program as a crutch rather using it as a creative tool.
Tony Searl

What is data science? - O'Reilly Radar - 1 views

radar.oreilly.com/...what-is-data-science.html

data datascience trends collaboration learning resources LAK11

shared by Tony Searl on 22 Dec 10 - Cached
  • how to use data effectively -- not just their own data, but all the data that's available and relevant
  • Increased storage capacity demands increased sophistication in the analysis and use of that data
  • Once you've parsed the data, you can start thinking about the quality of your data
  • ...20 more annotations...
  • It's usually impossible to get "better" data, and you have no alternative but to work with the data at hand
  • The most meaningful definition I've heard: "big data" is when the size of the data itself becomes part of the problem
  • Precision has an allure, but in most data-driven applications outside of finance, that allure is deceptive. Most data analysis is comparative:
  • Storing data is only part of building a data platform, though. Data is only useful if you can do something with it, and enormous datasets present computational problems
  • Hadoop has been instrumental in enabling "agile" data analysis. In software development, "agile practices" are associated with faster product cycles, closer interaction between developers and consumers, and testing
  • Faster computations make it easier to test different assumptions, different datasets, and different algorithms
  • It's easer to consult with clients to figure out whether you're asking the right questions, and it's possible to pursue intriguing possibilities that you'd otherwise have to drop for lack of time.
  • Machine learning is another essential tool for the data scientist.
  • According to Mike Driscoll (@dataspora), statistics is the "grammar of data science." It is crucial to "making data speak coherently."
  • Data science isn't just about the existence of data, or making guesses about what that data might mean; it's about testing hypotheses and making sure that the conclusions you're drawing from the data are valid.
  • The problem with most data analysis algorithms is that they generate a set of numbers. To understand what the numbers mean, the stories they are really telling, you need to generate a graph
  • Visualization is crucial to each stage of the data scientist
  • Visualization is also frequently the first step in analysis
  • Casey Reas' and Ben Fry's Processing is the state of the art, particularly if you need to create animations that show how things change over time
  • Making data tell its story isn't just a matter of presenting results; it involves making connections, then going back to other data sources to verify them.
  • Physicists have a strong mathematical background, computing skills, and come from a discipline in which survival depends on getting the most from the data. They have to think about the big picture, the big problem. When you've just spent a lot of grant money generating data, you can't just throw the data out if it isn't as clean as you'd like. You have to make it tell its story. You need some creativity for when the story the data is telling isn't what you think it's telling.
  • It was an agile, flexible process that built toward its goal incrementally, rather than tackling a huge mountain of data all at once.
  • we're entering the era of products that are built on data.
  • We don't yet know what those products are, but we do know that the winners will be the people, and the companies, that find those products.
  • They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: "here's a lot of data, what can you make from it?"
levoker89

Artificial Intelligence Change the Way Mobile Applications - 0 views

australianbusinessnetwork.wordpress.com/...es-the-way-mobile-applications

Artificial Intelligence AI Technologies Mobile APPS Machine Learning Mobile Applications Engineers and Software Developers AI Companies

shared by levoker89 on 13 Sep 19 - No Cached
  • levoker89 on 13 Sep 19
     
    Artificial intelligence will become a $38 billion industry by 2025 and companies across all the domain. AI companies that are looking forward to building and revolutionize AI within the industry.
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