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The beauty of data visualization - David McCandless

The beauty of data visualization - David McCandless
To create his infographic about nutritional supplements, it took McCandless a month to review about 1,000 medical studies and design the visual. Is that level of effort surprising, and do you think it’s worth it? Try out the interactive version that’s available on McCandless’s website. What engaged or surprised you? What, if anything, would you change to improve the user experience? Related:  Business Analytics

Blog Wired has a fascinating article out about a newly-released and free facial recognition tool that, coupled with existing video monitoring, claims to keep schools safer. From the article by Issie Lapowski: “RealNetworks has developed a facial recognition tool that it hopes will help schools more accurately monitor who gets past their front doors. Today, the company launched a website where school administrators can download the tool, called SAFR, for free and integrate it with their own camera systems … [F]acial recognition technology often misidentifies black people and women at higher rates than white men. “The use of facial recognition in schools creates an unprecedented level of surveillance and scrutiny,” says John Cusick, a fellow at the Legal Defense Fund. Glaser … is all too aware of the risks of facial recognition technology being used improperly. “I personally agree you can overdo school surveillance. The question is whether it will do any good. Read the full article here.

Hans Rosling - GapMinder Rosling is a passionate advocate for “liberating” publicly-funded data on the Internet. Select one topic area for which country-specific data might be compared (e.g., education, health, food production, the environment, etc.), and identify what you think are the best sources of data in this area on the Internet. Create a guide that lists these sources, and provides a brief review of each. If the administrators of these data repositories are thinking about how users might engage with the data via mobile devices or social media, note this in the review. Here are a few resources to make learning statistics an interesting experience. Someone always asks the math teacher, "Am I going to use calculus in real life?" The Institute for Statistics Education at is the leading provider of online education in statistics, and offers over 100 courses in introductory and advanced statistics.

Google Analytics 101: 3 Key Things You Can Learn Designing your new website, creating compelling content, and building out your social platforms is (sigh) the fun part. The sometimes not so fun—but just as important—part? Analytics! After all, if you don’t know how people are interacting with your brand, you won’t know how to make it better. So, where to begin? 1. Once you’ve installed Google Analytics on your website, you’ll have access to a Google Analytics dashboard. In addition to being really interesting, these stats can help you make business decisions moving forward. 2. Now that you know that (let’s say) 50,000 people are accessing your site each month, how long are those 50,000 visitors staying? Remember: The longer, the better! Next, taking a look at “New vs. Once you start tracking this metric, make a note of when it goes up or down. 3. Google Analytics offers an extremely valuable analytic called “Traffic Sources,” which explains how your visitors ended up on your site.

City Digits Project Breathingearth - Births / Deaths live data feed Addressing the Analytics Skills Gap On the final day of last week's MIT Symposium, the sole focus was on the skills gaps organizations are facing in trying to advance their analytics efforts – and what corporations and universities are doing about it. A great many companies are looking to hire talent for their analytics teams – but that talent is in short supply. Try searching for analytics jobs on any of the popular job boards. When I searched for “data analyst” on, I found over 80,000 job postings. We are seeing the skills crunch within HR organizations. One of the biggest skills gaps on these teams today is the ability to tell the story – someone who can take data or a statistical model and explain it to business leaders in terms of what it means, why they should care, and what they should do about it. In the meantime, what are HR leaders doing to bridge the skills gap within their organizations? Figure 1.

Why Kids Need Data Literacy, and How You Can Teach It Samantha Viotty’s activity for visualizing data networks has gummy bears representing people and toothpicks signifying their relationships. Photo courtesy of Samatha Viotty Data is all around us, from the output of your Fitbit to interactive maps that track voters to the latest visualization of the New York Times front page. With the rise of mobile devices and wearable technology, data is more available to general audiences, and the amount being generated has also exploded. According to IBM, 90 percent of the world’s data has been created in the last two years. This vast pool of information is being used to advocate for change, justify decisions, and suggest personal action plans—such as the U.S. One reason data literacy is vital is that “[i]n what some are calling a ‘post-truth world,’ students seem to focus on numbers a lot,” says Jo Angela Oehrli, learning librarian/children’s literature librarian at the University of Michigan Libraries. What are data literacy and data science? U.S.

What to Ask Your "Numbers People" - Tom Davenport by Tom Davenport | 9:00 AM July 12, 2013 If you’re a manager working with the analysts in your organization to make more data-driven business decisions, asking good questions should be one of your top priorities. Many managers fear that asking questions will make them appear unintelligent about quantitative matters. In my new book (co-authored with Jinho Kim) Keeping Up with the Quants, and in a related article in this month’s HBR, we list a lot of possible questions for various stages of analysis. 1.Questions about Assumptions You ask: What are the assumptions behind the model you built? You think in response to their answer: If they say there are no particular assumptions, you should worry — because every model has assumptions behind it. Follow-up: Is there any reason to believe that those assumptions are no longer valid? You think in response: You are really looking only for a thoughtful response here. 2. You ask: How are the data you gathered distributed? You get the picture.

Data Literacy for High School Librarians We’ve been busy tapping away on Creating Data Literate Students, a guide to integrate the “reading” and “writing” of data. How do students learn to “read” and “write” data with accuracy?Given how jam-packed high school curriculum is, it isn’t practical for teachers to add a unit or course on data or statistical literacy to the curriculum. What are the tips, rules of thumb, and guidelines that would make the greatest impact in the limited time available? Our chapters are now available for you to read and download, and by the end of Summer 2017, we’ll have copies available to purchase on Amazon or download in a machine-readable format, too. Don’t see your favorite contributor here? Cover IntroductionKristin Fontichiaro, Jo Angela Oehrli, Amy Lennex Chapter 1: Introduction to Statistical LiteracyLynette Hoelter Chapter 2: Statistical Storytelling: The Language of Data Tasha Bergson-Michelson Chapter 3: Using Data in the Research ProcessJole Seroff Glossary

What is big data? - Bringing big data to the enterprise TDWI Big Data Maturity Model and Assessment Tool Sponsored by IBM, this big data maturity online assessment tool enables organizations to objectively measure the maturity of an enterprise’s big data analytics program across five dimensions that are key to deriving value from big data analytics: organization, infrastructure, data management, analytics, and governance. Take it today! Big Data Hadoop Solutions, Q1 2014 Descritption: Read the report to see why IBM InfoSphere BigInsights was named a leader and how it stands in relation to other big data Hadoop vendors. Read the report The top five ways to get started with big data Learn how to determine which of the five can be your first step into big data. Get the white paper The FOUR V’s of Big Data IBM data scientists break big data into four dimensions: volume, variety, velocity and veracity. View the infographic Understanding big data Gain insight into IBM’s unique in-motion and at-rest big data analytics platform. Get the ebook Get the eBook

100 People: A World Portrait