Guest blog post by vHomeInsurance.
Spatial Visualization Using R: One of the less understood aspects of R is in spatial data visualization. The below article will outline two case studies on using R to spatially visualize data.
Case Study 1: Using Census Data & Chloropeth R
Our first step is figuring out how to use the Census API within R. Given…Continue
Added by Vincent Granville on January 29, 2015 at 11:30am — No Comments
It’s hard to think of a more worthwhile use for big data than saving lives – and around the world the healthcare industry is finding more ways to do that every day.
From predicting epidemics to curing cancer and making staying in hospital a more pleasant experience, big data is proving invaluable to improving outcomes.
This is very good news indeed – as the cost of caring has skyrocketed in recent years and is expected to continue to do so as the population ages – to the point…Continue
Over the course of a startup’s lifetime, a company can face a range of data challenges. In the beginning, startup founders do most analysis themselves using tools like Google Analytics. As a company grows, business and product leaders often have analytical questions that these tools—and busy executives—can’t answer.
For many companies, reaching this point is a signal to start building an analytics team. But who do you hire? An analyst might be able to deliver value right away, but…Continue
Added by Derek Steer on January 29, 2015 at 7:30am — No Comments
Data Scientist communities have their own complex jargon; multivariate regression models, Big data engineering, Hadoop, Map Reduce, Deep Learning etc. But, unfortunately businesses do not seem to care about how complex the term is or how impressive the math is! They want the results explained in non-tech terms.
While working on Big Data & planning to…Continue
The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday.
Added by Vincent Granville on January 28, 2015 at 5:00pm — No Comments
The sniper scopes in on a young grinning child holding up a gold Rolex. Its a long shot. To his right is the “institutionalist” and to his left is the “realist” both brandishing armor and holding a plethora of weapons. Their weapons are visible yet ineffective at this distance. The sniper is too far away way, way too accurate; deadly accurate. Yet at least one will survive as the effect of a long distance bullet is obvious, sudden and frightening.
The dark figure in crime data is the…Continue
Learn how to utilize multi-core, high-memory machines to dramatically accelerate your computations in R and Python, without any complex or time-consuming setup.
Added by Anna Anisin on January 28, 2015 at 12:30pm — No Comments
Imagine the following business problem:
A call center has a rule that if more than 8 customers calls in 24 hours about Issue X, then there should be an alarm & that that Issue X should be forwarded to Tier 2 team for further investigation. However, the Tier 2 team believes that 24 hours is too long to wait since the customer experience could suffer. They want to predict BEFORE the 24 hour interval. Therefore, they want the probability at any given time based on historical hourly…Continue
Added by Gridlex on January 28, 2015 at 2:30am — No Comments
Want to apply Data Science to Business Process Management? Have a look at process mining, explained in this poster!
Added by Linda Terlouw on January 27, 2015 at 2:00am — No Comments
The purpose of Financial Modeling is to build a Financial Model which can enable a person to take better financial decision.The decision could be affected by future cash flow projections , debt structure for the company etc. All these factors may affect the viability for a project or investment in a company.The Applications of Financial Modeling mainly includes the followings :
Added by rajesh dhnashire on January 26, 2015 at 11:00pm — No Comments
This is an update to our December 2013 article: 6000 companies hiring data scientists. Microsoft and IBM still dominate, but we've seen some shift over the last 12 months:
I always make the point that data is everywhere – and that a lot of it is free. Companies don’t necessarily have to build their own massive data repositories before starting with big data analytics. The moves by companies and governments to put large amounts of information into the public domain have made large volumes of data accessible to everyone.
Any company, from big blue chip corporations to the tiniest start-up can now leverage more data than ever before. Many of my clients ask…Continue
There is often confusion between the definitions of "data veracity" and "data quality".
Data veracity is sometimes thought as uncertain or imprecise data, yet may be more precisely defined as false or inaccurate data. The data may be intentionally, negligently or mistakenly falsified. Data veracity may be distinguished from data quality,…Continue
Often, Data Science for IoT differs from conventional data science due to the presence of hardware.
Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing).
Alternately, we see entirely…Continue
Added by ajit jaokar on January 25, 2015 at 12:30pm — No Comments
"Today, India ranks second worldwide in farm output. The economic contribution of agriculture to India's GDP is steadily declining with the country's broad-based economic growth. Still, agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India." - From Wikipedia…Continue
Added by VINU KIRAN .S on January 24, 2015 at 1:00am — No Comments
The years leading up to 2015 affirmed and reaffirmed that we indeed have lots of data. And with lots of data all around us, organizations should really be thinking about how to leverage data to get ahead not only of competition but to get ahead of everyday transactions and to be able to predict and control what's coming down the pike. Almost every transaction in society now is ‘datafied.' The automation and datafication of transactions and actions are giving us the opportunity to understand…Continue
Added by B.J. Gonzalvo, PhD on January 23, 2015 at 3:44pm — No Comments
The other day, I found myself feeling exceptionally tired, not getting much work done even though it was 11:30 in the morning.…Continue
Added by John Irvine on January 22, 2015 at 6:30pm — No Comments
If you work with data regularly, chances are you trust it. You know how it's collected and stored. You know the caveats and the roadblocks you face…Continue
Here is my selection of time-insensitive best articles, that were viewed more than a thousand times each, in 2014.
Single-starred articles are written by external/guest bloggers. Our upcoming book on data science 2.0 will be based on some of these (edited and…Continue
Added by Vincent Granville on January 21, 2015 at 1:53pm — No Comments