There are people who can imagine ways of using data to improve an enterprise—they can explain the vision, make it real, and affect change in their organization. The combination of skills this requires may seem mystical, magical, and imaginary, but no, these folks are not unicorns. They’re professional data scientists and engineers with the ability to make a real impact on the enterprise.
Join expert Jerry Overton as he walks you through how to get started “doing” data science in the enterprise, how to build and execute a data strategy, how to write algorithms, and how to experiment on an enterprise-scale.
This course is for you if you’re a data scientist, engineer, or architect who aspires to become better at visualizing data-driven improvements, making them real, and driving change in an organization.
Part 1: How to get started on the journey to pro
Going pro in data science
Forget about the stack
Think utility instead
Anatomy of the pipeline
Part 2: How to build (and execute) a data strategy
What’s a data strategy
Building a data strategy
Part 3: How to write algorithms like a pro
Why an algorithm is to a data scientist as a microscope is to a biologist
Tools of the pro
Things the pros know
Part 4: How to experiment on an enterprise scale
The dimensions of industrialized machine learning
Part 5: The top five habits of a professional data scientist or engineer
Jerry Overton is a data scientist and distinguished technologist in DXC’s Analytics Group, where he is the principal data scientist for industrial machine learning, a strategic alliance between DXC and Microsoft comprising enterprise-scale applications across six different industries: banking and capital markets, energy and technology, insurance, manufacturing, healthcare, and retail. Jerry is the author of Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist (O’Reilly) and teaches the O’Reilly training course Mastering Data Science at Enterprise Scale. In his blog, Doing Data Science, Jerry shares his experience leading open research and transforming organizations using data science.
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