Summary

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

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.

About the book

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

About the reader

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

About the author

Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor.

Table of Contents:

PART 1 - GETTING STARTED WITH DATA SCIENCE

1. What is data science?


2. Data science companies


3. Getting the skills


4. Building a portfolio
   
   PART 2 - FINDING YOUR DATA SCIENCE JOB


5. The search: Identifying the right job for you


6. The application: Résumés and cover letters


7. The interview: What to expect and how to handle it


8. The offer: Knowing what to accept
   
   PART 3 - SETTLING INTO DATA SCIENCE


9. The first months on the job


10. Making an effective analysis


11. Deploying a model into production


12. Working with stakeholders
    
    PART 4 - GROWING IN YOUR DATA SCIENCE ROLE


13. When your data science project fails


14. Joining the data science community


15. Leaving your job gracefully


16. Moving up the ladder