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…

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…

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…

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…

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. 

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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…

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…

Want to apply Data Science to Business Process Management? Have a look at process mining, explained in this poster!

Download high resolution version.

DSC Resources

• Career: …

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:

• Accenture, Google and Cognizant are gaining traction, but overall, rankings for top companies have barely changed
• The top 20 companies now amount to 7.5% of data scientists, versus 10%…

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…

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,…

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…

     "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…

​When are you at your peak?​

It's 6:00am. Alarm clock's ringing. Begrudgingly you get up, throw on a pot of coffee, take a shower and are still moving slow as you are out the door and off to work. By the time we get to work, we feel that we are ready for the day. 

The other day, I found myself feeling exceptionally tired, not getting much work done even though it was 11:30 in the morning.…

Building a successful analytics team requires investment in everything from people and tools to infrastructure and event tracking. But the biggest payoffs can often come from investing in credibility.

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…

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. 

Sponsored Announcement…

High resolution version can be found via www.icris.nl or here.

For any data science project, if you start with the wrong question, you are bound to end up with the wrong answer, and fail. Who should identify the right question? I believe data scientists should be involved in the process, otherwise, they will be held responsible for the failure.

CDC headquarters in Druid Hills,…

Guest blog post by Bernard Marr.

In my last post, I explained the difference between what I consider the two core types of data scientist – strategic and operational.

Broadly speaking, they require many of the same skillsets – but the distribution of your expertise and experience within these skillsets will vary, depending on whether…