AM4: Stepping Into Data Science
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  Charlie Vollmer   Charlie Vollmer
Senior Statistician
ML Engineering


Monday, September 19, 2016
08:30 AM - 11:45 AM

Level:  Introductory

What exactly is Data Science and what does it take to become a Data Scientist?

This tutorial will immediately delve into how Data Science differs from traditional BI and business analytics and discuss the skill sets that are different than these traditional roles. We will show varying use cases (across industries) which highlight why Data Science is the next generation of computation in enterprise Information Technology and -paired with the skill set- understand why the demand and median salaries for Data Scientists continues to rise.

This tutorial is an explicitly-crafted pathway for all those interested in learning about the field of Data Science and - more specifically - those interested in delving into that world. We will overview various use cases commonly found in disparate industries where data (small and big) provide an opportunity to inform decision making from customer base growth and marketing to product pricing and risk evaluation. We will then discuss the tools and techniques used to approach and, ultimately, solve these problems. From building statistical models to visualizing data using various tools (including Tableau), we will build the entire life cycle of a "data science product."

Attendees will leave with sufficient knowledge and hands-on experience to get started becoming a Data Scientist, along with the resources to take subsequent steps in the field.

All of the tools will come from the open-source community, using Python and R in Jupyter Notebooks. This enables fast, easy analysis and collaboration, along with free access for all attendees to develop their skills (not to mention the same tools that all Data Scientists use from Silicon Valley to London).

Charlie Vollmer is a Minnesota boy, at home in the mountains. When he is not teaching his seven-year-old how to ski powder, he is using mathematics to tell computers how to discover patterns in data. He believes anyone can do machine learning, and that by sharing information on computer science, we are all better off. He thinks that if you only give him the chance, he can teach you any statistical concept, and that you'll walk away actually thinking positively about math.

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