How to Get Hired as a Data Scientist (Video Series)

Katie and Jon from Galvanize host O'Reilly video series

You’ve just graduated from a data science immersive program. You’re a whiz at Python and SQL, can independently tackle data modeling and machine learning, and you’re eager to begin solving real world problems. First, you need a job. So, how do you prepare yourself for the #1 job in America?  More importantly, how do you stand out above all candidates?

A new O’Reilly video series, Cracking the Data Science Interview, answers these questions and more. Produced by Jonathan Dinu and Katie Kent, who helped found the data science program at Galvanize, this nearly three hour paid video series delves into everything you need to know to prepare for the interview process.

“The interviewing landscape changes very fast,” Kent said. “Data science means different things to different people and with hundreds of people applying for a position, you have to be prepared.”

In the series, a team of data science educators and job placement pros illustrate the technical concepts and explain the behavioral aspects you need for a successful data science interview. You’ll learn about the variety of data science job titles, the interview funnel, mastering mock interviews, and negotiating job offers.

From O’

This series covers the nuances, misconceptions, and realities of the data science job hiring process, including:

  • Explore the distinctions between job titles like data scientist, data analyst, and data engineer.
  • Discover the “Growth Hacking” technique that will help you land interviews.
  • Understand who the stakeholders are in the hiring process.
  • Craft a “what-people-will-remember-about-you-after-the-interview” sales pitch.
  • Learn how to identify the data science concepts to review before the interview.
  • Review techniques for passing the technical screen and what to do if you flub a question.
  • Understand how to analyze salary vs. equity compensation offers.

For more information, check out Galvanize’s supplemental page: The Data Scientist’s Guide to Interviewing.

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