Going Pro in Data Science

What It Takes to Succeed as a Professional Data Scientist

Going Pro in Data Science

Get the free ebook

Digging for answers to your pressing business questions probably won’t resemble those tidy case studies that lead you step-by-step from data collection to cool insights. Data science is not so clear-cut in the real world. Instead of high-quality data with the right velocity, variety, and volume, many data scientists have to work with missing or sketchy information extracted from people in the organization.

In this O’Reilly report, Jerry Overton—Distinguished Engineer at global IT leader DXC—introduces practices for making good decisions in a messy and complicated world. What he simply calls “data science that works” is a trial-and-error process of creating and testing hypotheses, gathering evidence, and drawing conclusions. These skills are far more useful for practicing data scientists than, say, mastering the details of a machine-learning algorithm.

Adapted and expanded from a series of articles Overton published on O’Reilly Radar and on the CSC Blog, each chapter is ideal for current and aspiring data scientists who want to go pro, as well as IT execs and managers looking to hire in this field. The report covers:

  • Using the scientific method to gain a competitive advantage
  • The skill set you need to look for when choosing a data scientist
  • Why practical induction is a key part of thinking like a data scientist
  • Best practices for writing solid code in your data science gig
  • How agile experimentation lets you find answers (or dead ends) much faster
  • Advice for surviving (and even thriving) as a data scientist in your organization

Please tell us who we’re sharing this with and we’ll email you the ebook.

All fields are required.

Please read our Privacy Policy.

Jerry Overton

Jerry Overton is a Data Scientist and Distinguished Technologist in DXC’s Analytics group. He is the Principal Data Scientist for the strategic alliance between DXC and Microsoft known as Industrial Machine Learning— enterprise-scale applications across six different industries: banking and capital markets, energy and technology, insurance, manufacturing, healthcare, and retail.

Jerry is the author of the O'Reilly Media eBook Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course Mastering Data Science at Enterprise Scale: How to design and implement machine-learning solutions that improve your organization. In his blog, Doing Data Science, Jerry shares his experiences leading open research and transforming organizations using data science.