While technical skill is undeniably important when approaching any data science effort, there is an art to data science and machine learning that doesn't seem to be discussed as often as pure technical skill is. These more soft skills help a seasoned data scientist navigate through numerous opportunities as seamlessly and efficiently as possible. The fact of the matter is that pretty much every data science effort has its own flair to it that poses unique challenges that may (or frankly, may not) be worth pursuing. Because applied data science always grounds itself in seeking a solution to a real world problem, having subject matter expertise about that real world problem is an absolute must. Of course, it's unreasonable to expect for a data scientist themselves to have that subject matter expertise themselves.

Organizations wishing to claim a share of the $19 trillion dollar opportunity projected from the Internet of Things (IoT) will need to accelerate preparation for the data-driven future. Executives are clear that Data and Analytics are top priorities. Industry experts predict that by 2020, 90% of large organizations will have hired a chief data officer (CDO.) However, it is projected that only 50% will be perceived as having been successful in executing on their charter. Currently, the greatest barrier to success is the lack of data expertise.

When is using Artificial Intelligence on data different from Data Science? The more autonomous the algorithm, the more Artificial Intelligence it contains. Basic Artificial Intelligence: Learning a pattern from data with a single Machine Learning algorithm and fixed representation. At this level, the Data Scientist has to try different Machine Learning algorithms, try different tuning parameters, and evaluate different problem features/attributes.Most Data Science efforts are at this level. Advanced Artificial Intelligence: Learning a pattern from data with a meta-learning algorithm applied to the space of Machine Learning algorithms and representations.