The Hadoop Summit conference, hosted by Hortonworks and Yahoo, has become a must-see Big Data event. The Hadoop distributed computing architecture is now an integral part of what it means to be a data scientist, and a few days of concentrated effort each year is enough to get a vision for where the industry is headed. The Hadoop Summit serves this purpose well by providing thought-provoking technical sessions, keynote addresses, and a vendor exhibition that brings many of the major players in the Hadoop ecosystem together under one roof.

Edited by Yan Zhang

Many pundits speaking about the state of the nation’s healthcare infrastructure routinely point to fraud, waste, and abuse (FWA) as major reasons for many of the problems the public witnesses every day – increased healthcare costs and the resulting increase in insurance premiums. The net effect is an annual loss of billions of dollars, and these losses affect the public in very real ways. 

The size of the healthcare sector, the enormous amount of money involved, and the lack of surveillance and monitoring mechanisms across the healthcare ecosystem make it an attractive target for FWA. According to the Office of Management and Budget, in 2010, about 9%, or $47.9 billion was lost to fraud in Medicare alone. It is therefore imperative to develop effective FWA technologies and solutions for reducing the costs associated with our healthcare system.

Data science and its primary enabling methodology of machine learning represent the country’s best chance for detecting FWA to avoid extraordinary sources of loss. Data science possesses the facilities to make a significant difference healthcare industry budgets and their impact to the public. Opera Solutions is the industry leader in applying Big Data technologies to the most challenging and significant business problems. We are the company charged with developing the analytics to identify fraud for the Centers for Medicare & Medicaid Services (CMS) on the health insurance exchanges. Here’s a look into just how big this challenge is — and some of the approaches we’re taking to overcome one of the costliest burdens in America.

Ever wonder how services like Netflix or Pandora choose media to suggest to you? If you’ve been reading this blog for a while, you’re familiar — at least a little bit — with recommender engines. In our post “How Machine Learning Will Affect Your Next Vacation,” we talked about the impact machine-learning recommender engines have on regular consumers. But here, we want to dive deeper and talk about the math and science behind recommender engines.

As a relatively new term, “data science” can mean different things to different people due in part to all the hype surrounding the field. Often used in the same breath, we also hear a lot about “big data” and how it is changing the way that companies interact with their customers. This begs the question — how are these two technologies related? Unfortunately, the hype can cloud our understanding for how these technologies are working to shape our increasingly data-driven society. Rest assured, there truly is something deep and profound representing a paradigm shift in our society surrounding data, but the hype isn’t helping to clarify data science’s exact role in Big Data. In this article, we strive to put to rest many of the misunderstandings surrounding data science.

Conversion rates for online retailers are generally considered pretty dismal by most common measures. Imagine if only 1–3 percent of shoppers entering your store ended up making a purchase. Maybe you’d think of trying a new strategy. The new strategy employed by many online retailers is called retargeting — the use of search and display campaigns to target the 97 percent of visitors who came to your e-commerce site but didn’t convert, meaning they did not make a purchase, fill out a form, or request a demo or call. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit other sites online.

Be careful what you wish for. Most of us probably heard that phrase at some point during our childhood, or perhaps even more recently. The point is valid. When wishing for something we tend to focus on the positives while ignoring the potential negatives. After all, who would wish for something that had a downside?

When people shop online, they know they’re sharing personal data to be collected and used for marketing purposes. And yet we’re surprised by how many companies fail to do even the bare minimum of analysis with the data they’ve painstakingly gathered. By implementing just a few basic analytics techniques into your marketing practices, you can quickly gain insight into your site’s performance, your customers’ satisfaction, and the effectiveness of your marketing campaigns. And if you take your analytics a few steps further, you can actually predict what your customers want and gain specific recommendations about how best to market to them — whether you have hundreds of customers or millions of them.

Here, we’ll show you the basic building blocks of retail marketing analytics. The insights gathered from these techniques can help you optimize business strategies and increase revenues. We’ll show you which metrics, or key performance indicators (KPIs), matter the most and how they can be used to measure the success of a retail enterprise. We’ll also shed some light on what’s possible once you establish the basics: the more advanced analytics techniques that deliver predictive insights and recommended actions. Your crash course starts now.

Switching to a new medical coding system won’t be easy, but when combined with data science and machine learning, ICD-10 presents enormous potential benefits for both the financial and the clinical sides of healthcare.

Part of why the healthcare industry is such a notorious laggard in jumping on the Big Data bandwagon is that every attempted change faces a huge domino effect, rendering many good ideas useless until everyone — and everything — is ready. One big step in the right direction, however, is an important upgrade to the computerized codes used for electronic medical records (EMR), which will take hold in the next year or two. These codes, known as ICD or International Classification of Diseases, determine what ailments patients have and how much they and their insurers should pay for a treatment. The set of codes, currently called ICD-9, is scheduled for its 10th revision this fall (but there may be a year-long delay). The updated codes, called ICD-10 codes, improve on the previous standard by adding more descriptive capabilities that will help healthcare professionals better categorize and keep track of patient disorders and treatments. Through the use of machine learning and other data science techniques, this increased granularity is expected to open up patient treatment analytics along with the ability to better monitor public health threats.

Way back in 1982, Thomas Dolby famously quipped “She blinded me with science and hit me with technology.” While musical tastes have since changed, the sentiment is stronger than ever. Science, especially data science, can be an intimidating subject for marketers and other non-mathematicians.

Think you know English? Think again. See if you have what it takes to teach a computer how to understand humans.

Anyone who has tried to learn English as a second language is only too familiar with its many — many — challenges. In addition to idioms, sarcasm, and a wide array of meanings when combined with various prepositions (think: make up, make out, make it, and of course, makeup), there’s also pop culture, trends, products, and more to keep straight. Luckily for us, we’re human, and even those well established in their native languages will be able to speak and decipher English with enough practice and exposure. But what about machines? How do we even begin to program them in a way that they can read and understand sentiment? Answer: very carefully. The process requires machine learning data scientists to use Natural Language Process (NLP) techniques, a form of advanced analytics. They use these techniques to build models that can decipher sentiment and weed out the meaningful information among the noise.