Build a Career in Data Science

  • 7h 58m
  • Emily Robinson, Jacqueline Nolis
  • Manning Publications
  • 2020

What are the keys to a data scientist's long-term success? Blending your technical know-how with the right "soft skills" turns out to be a central ingredient of a rewarding career.

Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you'll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You'll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book.

What's inside:

  • Creating a portfolio of data science projects
  • Assessing and negotiating an offer
  • Leaving gracefully and moving up the ladder
  • Interviews with professional data scientists

For readers who want to begin or advance a data science career.

About the Authors

Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers.

Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.

In this Book

  • About This Book
  • About the Cover Illustration
  • What is Data Science?
  • Data Science Companies
  • Getting the Skills
  • Building a Portfolio
  • The Search—Identifying the Right Job for You
  • The Application—Résumés and Cover Letters
  • The Interview—What to Expect and How to Handle It
  • The Offer—Knowing What to Accept
  • The First Months on the Job
  • Making an Effective Analysis
  • Deploying a Model into Production
  • Working with Stakeholders
  • When Your Data Science Project Fails
  • Joining the Data Science Community
  • Leaving Your Job Gracefully
  • Moving up the Ladder
  • Epilogue
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