Preface

You are reading this probably because you are aware of the importance of machine learning and advanced analytics, such as predictive analytics. While there is an increasing demand for people all over the world who possess these skill sets, there is a real scarcity of data scientists who are skilled enough to deliver applications that involve machine learning and advanced analytics and can create real value from the available data.

The reason for this scarcity is because the field of machine learning and data mining used to be the realm of PhDs and experts in subjects such as math, statistics, and programming combined. It's really difficult to find such unicorns. Again, tasks such as predictive analytics have historically been so difficult that even experts, even if they don't exactly struggle, don't find it easy either. This means that years of experience are needed for newcomers to to get on with it.

In this modern age, predictive analytics is on the verge of being industrialized as it is the key to sustaining and promoting the growth of a business. While the scarcity of "unicorn" data scientists doesn't seem to be ending, organizations are now finding solutions to get over this problem. A leading IT research firm, Gartner, suggests that, in the coming days, a new breed of professionals will emerge, referred to as citizen data scientists. Their emergence may bring about such a change that they may soon outnumber unicorn data scientists by a ratio of 5:1.

You might be wondering now, who are these citizen data scientists and where have they come from? They are existing developers, people from the business analyst community, and, possibly, new graduates as well, who are data-savvy, passionate about advanced analytics, and determined to stretch themselves and go in-depth into data science concepts. They will democratize data science and enable the industrialization of advanced analytics.

All this is happening and will continue to happen because of one reason: the arrival of new tools and platforms that make advanced analytics so easy and present data science as a commodity. While this brings huge opportunities for such vendors, it also bring good news for organizations and professionals who are picking it up. There is no doubt that Azure Machine Learning is a leader in this field and Microsoft offers this to organizations, strategically.

Microsoft's corporate vice president, Joseph Sirosh, who is in charge of Azure Machine Learning, describes Azure Machine Learning, as published in CITEworld: "This is the fastest way to build predictive models and deploy them. Very few tools exist today if you're going to build solution on the cloud and create applications. This way you can build intelligent applications from data, then publish as APIs so you can hook them up very easily from any enterprise application—and even from mobile. We're building it simple enough for a high schooler to be able to use it."

This book is an attempt to extend this vision; driven by simplicity, it sets the mission to develop the necessary skills to get started with Microsoft Azure Machine Learning as quickly as possible. The book assumes no prerequisites other than high school math!