Note: We address real time analytics in the Data Science for IoT practitioner’s course
I am a fan of Twitter - but Twitter is in the news for the wrong reasons and it… Continue
Added by ajit jaokar on October 21, 2015 at 3:44am —
The Internet Governance (IG) Barometer methodology presents a quantitative summary of the main developments in the Internet Governance arena based on computational text and data-mining approaches. The IG Barometer is based on the statistical modeling of large collections of textual documents: thus, it essentially presents an advanced discourse processing system. These collections - called text corpora - are obtained by querying various online media sources with… Continue
Added by Goran S. Milovanović on October 20, 2015 at 11:00pm —
Added by Teglor on October 20, 2015 at 8:00am —
Guest blog by Alleli Aspili at Infinit Datum
Big data became more than a buzzword when industries discovered its huge potential. And with this finding, came a new science called data analytics.
The demand to understand all the… Continue
Added by William Vorhies on October 20, 2015 at 7:30am —
Added by William Vorhies on October 20, 2015 at 7:00am —
This article describes the approach undertaken by data scientists at Axibase to calculate the Cloud Cover using satellite imagery from the Japanese Himawari 8 satellite.
Today, cloud cover is measured using automated weather stations, specifically the ceilometer and the sky imager instruments. The ceilometer is an upward pointed laser that calculates the time required for the laser to reflect from the clouds, determining the height of the cloud base.… Continue
Added by Axibase Corp on October 20, 2015 at 6:21am —
These are quotes by a well known data scientist, posted on his Facebook account and elsewhere, over the last three years. It includes both quotes related to data science as well as on how to become successful and happy.
Better: write "No SQL… Continue
Added by L.V. on October 19, 2015 at 4:30pm —
Data-as-a-Service Dictionary: 5 Terms You Need to Know
A few months ago, we posted this…
Added by Larisa Bedgood on October 19, 2015 at 9:22am —
If you’re fortunate enough to have a quantitative PhD, you may have thought about applying for an opening in data science. After all, it’s an exciting and well paid field and a lot of… Continue
Added by William Vorhies on October 19, 2015 at 7:29am —
Added by Demnag on October 19, 2015 at 2:00am —
We have discussed Zipf's law last year, as well as in one of our recent weekly challenges: this statistical distribution is a perfect model to explain so many physical or man-made phenomena. In this article, we publish a formula to measure the… Continue
Added by Vincent Granville on October 18, 2015 at 7:10pm —
Big data is not a fad. We are just at the beginning of a revolution that will touch every business and every life on this planet.
But loads of people are still treating the concept of big data as something they can choose to ignore — when actually, they’re about to be run over by the steamroller that is big data.
Don’t believe me? Here are 20 stats… Continue
Added by Bernard Marr on October 17, 2015 at 8:30am —
You don’t have to be a data scientist to be data savvy. And that’s a good thing.
Many companies are putting massive focus on recruiting the rare beasts that are data scientists. But in doing so, they often forget the need for creating a much more data savvy culture overall.
Data is already becoming ubiquitous in business as well as in daily life. It… Continue
Added by Bernard Marr on October 17, 2015 at 8:30am —
I routinely ask people who have degrees the following question: "So what did you do your thesis on?" Since I routinely encounter problems outside my domain, I like to be aware of the resources around me. I have been reminded that a student doesn't necessarily have to complete a major research paper to earn a degree. A student can just "do the program." As a person who has always chosen to do the research paper, I can say that this normally takes a fair amount of collaboration. There is a… Continue
Added by Don Philip Faithful on October 17, 2015 at 4:57am —
Finally I got some time to share what I learned during the Hadoop summit this year. 3 days, more than 170 talks, a lot to share and digest, overwhelming with all those new tech and ideas…. I hope I can briefly and clearly describe the vision I gathered during the Hadoop summit with the you… Continue
Added by Alice Xiong on October 17, 2015 at 4:30am —
Each organizations these days is after collecting data about its customers. But very few use this data to optimum use.
Most of the organizations are clueless about the huge chunk of data that is available with them. Many companies expect the data to answer their questions. But they forget that data doesn't answer on its own. It is the analytics which help the companies give meaning to the data and provide solutions to the queries. So it is important to know the questions one has to… Continue
Added by Aureus Analytics on October 16, 2015 at 10:30pm —
This article is no longer available. We apologize for the inconvenience. To read more about data science and story telling, click here.
Added by William Vorhies on October 16, 2015 at 9:00am —
There has been a growing trend among software engineers to write about their experiences building data infrastructures. Software engineers at Asana wrote about their experience last November; Spotify… Continue
Added by Yevgeniy Slutskiy Meyer on October 16, 2015 at 8:44am —
Gartner released their Magic Quadrant for operational database management systems. As expected the leaders in this market space in terms of completeness and execution are the well-established solutions such as Oracle, Microsoft and Amazon Web Services.
Although the robust and well-established solutions are still the preferred choice in the market, Gartner indicates that the open-source solutions are making way into the market as well. It is predicted that by 2018 more than 70%… Continue
Added by Zygimantas Jacikevicius on October 16, 2015 at 5:00am —
It looks at cluster analysis as an analysis of variance problem. This method involves an agglomerative clustering algorithm. It starts out with n clusters of size 1 and continues until all the observations are included into one cluster. This method is most appropriate for quantitative variables, and not binary variables.
Added by Alice Xiong on October 16, 2015 at 3:30am —