There's no doubt that the data science industry has come along way just in the last ten years, but you might be surprised that there is still a lot of growth potential in existing companies today. Perhaps one big reason for that is that we consistently face a shortage of qualified individuals, but I think another reason is that non-practitioners don't really understand the value that data science and artificial intelligence can bring. They hear the words "AI" or "machine learning" and associate those to Hollywood stereotypes like HAL from 2001: A Space Odyssey or Skynet from the Terminator movies. Of course, data science practitioners recognize that those Hollywood AIs represent a fictionalized potential for Artificial General Intelligence (AGI), but there's a lot more to this space than a talking computer. From random forest classifiers working well with structured data to deep learning working with unstructured data like text or images, there are a lot of different ways a data scientist can bring value to the table.

Today, big and small companies around the world are racing to adopt the latest tools in artificial intelligence and machine learning. While data is often positioned as the blanket cure for every business malady, those who work in the field understand all too well that data science algorithms are never a one-size-fits-all solution. As the field rapidly evolves, there are a growing number of advanced algorithms available for businesses to deploy in their day-to-day operations. From tools based on deep neural networks, clustering algorithms to time-series analysis, these solutions can resolve a wide range of business problems. However, out of this mass of options, the biggest challenge for an organization may be as simple as sourcing the right data and asking the right questions.

"Uber is making decisions in real time at global scale, while needing to take into account local nuances of the marketplaces," explained Franziska Bell, Senior Data Science Manager on the Platform Team at Uber. "And, of course, we also want to incorporate the user preferences on the product." As a result, Uber has invested heavily in data science, and Bell outlined some of Uber's data science strategy last month at the AI World Conference & Expo in Boston. Uber employs hundreds of data scientists working across the company, and Bell reports constant efforts to, "increase the innovation and speed with which these data scientists move." To speed up the rate of data science at Uber, the company has taken a dual approach: first to maximize each step of the existing data science project life cycle, and second to commoditize data science by creating platforms applicable to multiple use cases that are transferable and reusable. Data science projects at Uber fall into four life cycle stages, Bell explained: data exploration, iterative prototyping, productization, and finally monitoring.

A groundbreaking change is actually taking place in society and it includes information science. All people, from little area businesses to global enterprises, is actually beginning to understand the possible of data science and is actually discovering the importance in digitizing the data assets of theirs and becoming data driven. Irrespective of business, businesses have embarked on a similar trip to check out how you can run business value that is new by making use of analytics, machine learning (ML), and artificial intelligence (AI introducing information and) methods science as a brand new discipline. Nevertheless, though using these brand new technologies can help businesses simplify the operations of theirs and drive down costs, nothing is easy about obtaining the strategic method right for the information science investment of yours. This cheat sheet offers a peak at the basic ideas you have to remain in addition to when creating the information science technique of yours.

HealthTech presents a huge opportunity, but how can businesses leverage these advantages, and how can Data Science help? The healthcare industry is huge and in turn, the size of the Health Tech industry is significantly larger than in other fields. In the USA alone, the healthcare market is more than 17% of GDP. A recent article published by TechCrunch highlights the vulnerabilities in the industry based on its consistent and rapid expansion: "the healthcare market represents $3 trillion, almost 20 percent, of the U.S. economy. This market also is plagued by a level of gross inefficiency and under-performance largely unseen in any other industries in our post-internet world".

As digital marketing strategies grow more sophisticated and complex, businesses find themselves wrestling with more data than ever. That's a good problem to have. But an embarrassment of data riches isn't worth much when you don't have the tools to put it to use. Analytics tools help, is there more you could be doing? Enter your new best friend: The marketing data scientist.

There's no doubt in my mind that machine learning (ML) as part of a data science strategy can help revolutionize many aspects of everyday life. Below I highlight a few examples of how different industries are able to leverage machine learning for competitive differentiation and customer benefit. There are tens of thousands of daily published journals and papers across the world. It is impractical for every clinician to read and absorb these. ML can help identify patterns and correlations that humans alone would otherwise miss -- possibly resulting in diagnosis and treatment plans that are suboptimal.