The reason a lot of these companies are tagging their selfies with #machinelearning is that they have some cool algorithm. Sweet I.P. bro! News flash: algos are not intelligent. Algos take in data that you hand-picked, and probably pre-formatted, complete some operation you specified explicitly, and produce results which are predictable.Intelligence is not predictable. Intelligence does whatever IT thinks is best.
Read on to learn about how we built this visualization, and what we’ve learned about Game of Thrones—and its fans—from it. And for any of you who aren’t caught up… Warning! The rest of this post contain spoilers from Game of Thrones.
#GameOfThrones interactive visualization to see how the audience relates to the characters relate and them to each other, their popularity & emojis used about them helps track certain sentiments.
To those with their ears attuned to fissures in the media world related to data journalism, the use of the word “data” was pointed. That, plainly, was what Silver responded to. The site’s election podcasts generally feature Silver and several other of the site’s election team discussing the race, with particular attention paid to polls.
As online users, we’ve become accustomed to the giant, invisible hands of Google, Facebook, and Amazon feeding our screens. We’re surrounded by proprietary code like Twitter Trends, Google’s autocomplete, Netflix recommendations, and OKCupid matches. It’s how the internet churns. So when Instagram or Twitter, or the Silicon Valley titan of the moment, chooses to mess with what we consider our personal lives, we’re reminded where the power actually lies. And it rankles.
How should digital news organisations respond to this? Some say it is simple – “Don’t read the comments” or, better still, switch them off altogether. And many have done just that, disabling their comment threads for good because they became too taxing to bother with.
But in so many cases journalism is enriched by responses from its readers. So why disable all comments when only a small minority is a problem?
At the Guardian, we felt it was high time to examine the problem rather than turn away.
+NOTE: some really great interactive visualizations go along with the data accompanying this article at the link below:
1.1 Billion NYC Taxi and Uber Trips analyzed and visualized
An open-source exploration of the city’s neighborhoods, nightlife, airport traffic, and more, through the lens of publicly available taxi and Uber data.Images are clickable to open hi-res versions.
Turning raw data into actionable insights has been the goal of many a business since the 1990s. Back then, ‘business intelligence’ was the buzzword, and since the tweenies and teenies it’s been ‘big data’. Now the two are combining (‘Big Intelligence’, maybe?) as the promise of big data is finally capable of delivering the business intelligence that companies have long dreamed about. Read More
Summary: A good friendship or romantic partnership takes work. The same goes for customer relationships. Today’s consumers are looking for brands with experiences that feel personalized and effortless and will last long beyond the transaction. Great customer service keeps your customer relationships strong. And it can keep the love (of your brand) alive.
Today, you’d be lucky to find a cheap knockoff in a world dominated by crappy promotional infographics churned out for viral attention. Nicholas Felton, the data viz guru who once designed Facebook’s Timeline, now builds apps. Jer Thorp is as interested in reverse-engineering algorithms and data art as he is in producing pure data visualization. Even the infographics on the portfolio-sharing site Behance are on the downswing. “Infographic posting generally rose steadily from 2007 to 2012, where it peaked, and has begun to decline since then,” Sarah Rapp, Head of Behance Community Data & Insights, Adobe
All About Hadoop:
This Infographic Explains How it Works from DataViz
Originally posted on Udemy. It explains the concept, alternatives and career advice.
Six Sources of Big Data:
The information being generated from Big Data can be segmented into six specific categories:
- Web Mining: Data compiled by mining the open web. This includes automated processes of discovering and extracting information from Web documents and servers, including mining unstructured data. This can be information extracted from server logs and browser activity, information extracted about the links and structure of a site, or information extracted from page content and documents.
- Search Information: Data available as a result of browser activity tracking search and intent behavior. This data also identifies digital audiences through onboarding (matching consumers to their online IDs)
- Social Media: The average global Internet user spends two and a half hours daily on social media. A vast array of data is available on personal preferences, likes, “check-ins,” shares, and comments users are making.
- Crowd Sourcing: This is collective intelligence gathered from the public. Data is compiled from multiple sources or large communities of people, including forums, surveys, polls, and other types of user-generated media.
- Transactional: Data that is created when organizations conduct business, and can be financial, logistical or any related process involving activities such as purchases, requests, insurance claims, deposits, withdrawals, flight reservations, credit card purchases, etc.
- Mobile: Mobile data is driving the largest surge in data volume. It isn’t only a function of smartphone penetration and consumer usage patterns. The data is also created by apps or other services working in the background.
Big Data holds great promise for enterprises of all sizes. Read More
There is a category of vendors on the rise in the API space; Integration Platform as a Service (iPaaS). The term is fairly new and there is some debate as to the definition and features of vendors that would be classified in the iPaaS category. This post is an overview of iPaaS and features several vendors that offer iPaaS solutions. Read More