A day in the life of a data scientist in an AI company

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.

With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the “sexiest job of the 21st century.” Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses. Francesca and Jaya begin by outlining the typical skillset an exceptional data scientist needs. They then explore common applications of machine learning and artificial intelligence in different business verticals and explore why some companies are much more successful than others at driving analytics-based business transformation. Francesca and Jaya dive into a couple of specific use cases to demonstrate how machine learning and artificial intelligence can help drive business impact within an organization and how the right technology platform can boost employee productivity and help them innovate and iterate rapidly. You’ll learn why a modern cloud analytics environment that makes it easy to collect data, analyze, experiment, and quickly put things into production with a targeted set of customers is becoming a must-have for data-driven organizations and walk through a detailed use case, from how the data typically gets collected to data wrangling, building a model, tuning the model, and operationalizing the model for a business to use in their production environment. Francesca and Jaya share a framework to help you improve your data science skillset, systematically discover opportunities to create value from data, qualify new opportunities and assess their fit and potential, smoothly implement end-to-end advanced analytics pilots and projects, and produce sustainable ongoing business value from data. They conclude with a demo of an end-to-end advanced analytics solution built with R, Python, and Microsoft AI and share resources and tools for further learning and exploration.