The Power of Third-Party Marketing Data
The first thing to sort out when considering a data strategy is what types of data you have, what types you need, and how you can use both. The two main segments of data are first-party and third-party.
First-party is the foundation of your marketing strategy. This is the internal…
Added by Larisa Bedgood on March 24, 2016 at 10:30am —
During the last few years, the hottest word on everyone’s lip has been “productivity.” In the rapidly evolving Internet world, getting something done fast always gets an upvote. Despite needing to implement real business logic quickly and accurately, as an experienced PHP developer I still spent hundreds of hours on other tasks, such as setting up database or caches, deploying projects, monitoring online statistics, and so on. Many developers have struggled with these so called miscellaneous… Continue
Added by Irina Papuc on March 24, 2016 at 9:00am —
A simple way to find great articles and resources on popular subjects such as data science, machine learning, deep learning, Python, R , data sets, dataviz, IoT, AI - or even Excel - is to use our data science search engine. This page, populated with pre-selected queries, is an excellent starting point. The search box can be found on DSC and all our channels, on all pages,… Continue
Added by Vincent Granville on March 24, 2016 at 9:00am —
"Hey, Andy, check out this data I have. What's the best chart to show it?"
I get asked this question a lot. My answer is always the same: It depends. This is partly because it depends on the audience, the purpose, and the type of data you have. What is most important is that your choice of chart is determined by the story within the data just as much as it is by anything else. Just because you have geographical data, it doesn't mean you should make a map.
Added by Andy Cotgreave on March 24, 2016 at 9:00am —
Starred articles are new additions or updated content, posted between Thursday and Sunday. The weekly digest has six sections: (1) Featured Resources and Technical Contributions, (2) Featured Articles and Case Studies, (3) From our Sponsors, (4) News, Events, Books, Training, Forum Questions, (5) Picture of the Week, and (6) Syndicated Content.
The full version is always published Monday.… Continue
Added by Vincent Granville on March 23, 2016 at 10:00am —
Summary: Which is the most critical element in data exploration, statistics or data visualization? The answer is a little like the lyric ‘love and marriage, you can’t have one without the other’. It can be tempting to skip the data visualization but it’s frequently the key to making sure we aren’t heading down the completely wrong path.
Added by William Vorhies on March 23, 2016 at 8:35am —
This is a guest repost by… Continue
Added by Shawnee Swarengin on March 23, 2016 at 5:30am —
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.
On any given day, a data scientist may be required to:
Added by DR. Elvin Prasad on March 23, 2016 at 5:00am —
- Brush up on your math and statistics skills. A good data scientist must be able to understand what the data is telling you, and to do that, you must have solid basic linear algebra, an understanding of algorithms and statistics skills. More advanced mathematics may be required for certain positions, but this is a good place to start.
- Understand the concept of machine learning. Machine learning is emerging…
Added by DR. Elvin Prasad on March 23, 2016 at 5:00am —
If you are not an expert on Analytics then you must be curious about how one can use analytics for better optimization, forecasting and reporting. Or how HR can benefit by investing for stronger analytics?
But before understanding further, it is very important to know what HR analytics is!
HR analytics, also called talent analytics, is the application of considerable data mining and business analytics techniques to human resources data. The goal of human resources… Continue
Added by Soumyasanto Sen on March 23, 2016 at 4:30am —
Guest blog post by Harry Powell, Head of Advanced Data Analytics at Barclays.
I was at a meetup in Oxford recently and one of the speakers, the CEO of a tech start-up, brought up the subject of Data Scientists’ pay. Apparently they are paid too much. I am not sure whether the data supports this assertion, but it seems to be a common complaint amongst highly-paid CEOs. What… Continue
Added by Vincent Granville on March 22, 2016 at 5:30pm —
We all know the Internet loves cats.
Cats that eat cheezburgers, cats that dance, cats that play piano… And cats that can tell strangers where you live.
OK, the cat’s don’t actually talk. But by using the meta-data embedded in all those 15 million cat photos and videos a project called iknowwhereyourcatlives.com has plotted the physical locations of more than a million cats.
And it’s a much scarier and more sobering… Continue
Added by Bernard Marr on March 22, 2016 at 4:30pm —
In a recent Economist Intelligence Unit survey of 476 executives from around the globe, more than a third of respondents said that their companies suffered significant data losses over the course of last 12…
Added by Nate Vickery on March 22, 2016 at 5:00am —
Summary: This is my favorite IoT story. We are so used to IoT platforms being physical objects that we forget about the potential for biologics. In terms of direct economic reward little will compare to this story about the IoT and cows.
This is my favorite IoT story which I first heard from Joseph Sirosh, CVP of Machine Learning for Microsoft at the spring Strata convention in San Jose. We are so used to IoT platforms being physical objects like cars… Continue
Added by William Vorhies on March 21, 2016 at 7:43am —
The following use case shows how an exchange of clean blended data can simplify the implementation of commercial and strategic alliances between businesses with a common interest.
USE CASE 1
The company is a Professional Sports Club. They operate their own sports stadium which is well… Continue
Added by Bruce Robbins on March 21, 2016 at 6:00am —
Anyone who attended statistical training at the college level has been taught the four rules that you should always abide by, when developing statistical models and predictions:
- You should only use unbiased estimates
- You should use estimates that have minimum variance
- In any optimization problem (for instance to compute an estimate from a maximum likelihood function, or to detect the best, most predictive subset of variables), you should always shoot for a…
Added by Vincent Granville on March 19, 2016 at 4:30pm —
(At Lana's apartment)
- Lana: I like Roger. I think he could make me happy. (Hypothesis)
- Steph: By no chance. He’s a professional cheater, I am pretty sure about it. (Another hypothesis)
- Lana: I am so confused… What's more important: His loyalty to me or his ability to make me happy? (Data Science’s uttermost matter: Are we asking the right…
Added by Elías De La Rosa on March 19, 2016 at 12:43pm —
Interesting article posted recently in MIT Technology Reviews. What kind of metrics would help detect such tweets? We think the following might be useful:
- Local time (like late at night)
- Whether a picture or not is associated with the tweet
- Whether a link or not is associated with the tweet
- Number of typos for the tweet in question, compared with average for the user in question
- Frequency of tweets (sudden spike) for user in…
Added by L.V. on March 18, 2016 at 6:30am —
This is a guest repost by Jacob Joseph from CleverTap.
The most widely used coordinate system to represent data is the… Continue
Added by Shawnee Swarengin on March 18, 2016 at 12:00am —
The future of business, it is argued, is digital. At the core of this digital transformation is the ability to harness data in enabling better business decisions. Typically, organizations have teams of experts who work on existing data sets to apply diverse analytic tools and techniques to make sense of the data. The more statistically advanced among these teams work on typical ‘data science’ problems. Data science problems are where you need to apply sophisticated algorithms on large data… Continue
Added by Archisman Majumdar on March 17, 2016 at 11:30pm —