Build Your Career in Data Science
Emily Robinson and Jacqueline Nolis
  • MEAP began May 2019
  • Publication in Early 2020 (estimated)
  • ISBN 9781617296246
  • 250 pages (estimated)
  • printed in black & white

The survival handbook of aspiring data scientists and people thinking about their careers in data science, machine learning, and many other fields.

Brynjar Smári Bjarnason
You are going to need more than technical knowledge to succeed as a data scientist. Build Your 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.
Table of Contents detailed table of contents

Part 1: Getting Started with Data Science

1 What is data science?

1.1 What is data science?

1.1.1 Mathematics/statistics

1.1.2 Databases/programming

1.1.3 Business understanding

1.2 Different types of data science jobs

1.2.1 Business Intelligence

1.2.2 Machine Learning

1.2.3 Decision Science

1.3 Choosing your path

1.4 Interview with Robert Chang on what data science is and his journey

1.4.1 Can you tell us about your current job?

1.4.2 What was your first data science journey?

1.4.3 What should people look for in a data science job?

1.4.4 What’s your definition of a data scientist?

1.4.5 What skills do you need to be a data scientist?

1.4.6 How do you know if you would like being a data scientist?

1.4.7 What’s your final piece of advice to aspiring and junior data scientists?

1.5 Summary

2 Data science companies

2.1 MTC — the Massive Tech Company

2.1.1 Your team: one of many in MTC

2.1.2 The tech: advanced, but siloed across the company

2.1.3 The pros and cons of MTC

2.2 HandbagLOVE — the established retailer

2.2.1 Your team: a small group struggling to grow

2.2.2 Your tech: a legacy stack that’s starting to change

2.2.3 The pros and cons of HandbagLOVE

2.3 Seg-Metra — the early-stage startup

2.3.1 Your team—​what team?

2.3.2 The tech: cutting edge technology that is taped-together

2.3.3 Pros and cons of Seg-Metra

2.4 Videory — the late-stage, successful tech start-up

2.4.1 The team: specialized but still room to move around

2.4.2 The tech: trying to not get bogged down by legacy code

2.4.3 The pros and cons of Videory

2.5 Global Aerospace Dynamics (GAD) — the massive government contractor

2.5.1 The team—​a data scientist in a sea of engineers

2.5.2 The tech: old, hardened, and on security lockdown

2.5.3 The pros and cons of GAD

2.6 Putting it all together

2.7 Interview with Randy Au on different types of companies

2.7.1 What was your path to become a data scientist?

2.7.2 Are there big differences between large and small companies?

2.7.3 When you got to Google after working at start-ups, were you surprised by the differences?

2.7.4 Are there differences based on the industry of the company?

2.7.5 Should new data scientists be wary of startups since they’ll do tons of cleaning, pipeline creation, and unglamorous work?

2.7.6 What’s your final piece of advice for aspiring and junior data scientists?

2.8 Summary

3 Getting the Skills

3.1 Earning a data science degree

3.1.1 Choosing the school

3.1.2 Getting into an academic program

3.1.3 Academic degree summary

3.2 Going through a bootcamp

3.2.1 What you learn

3.2.2 Cost

3.2.3 Choosing a program

3.2.4 Data science bootcamp summary

3.3 Getting data science work within your company

3.3.1 Learning on the job summary

3.4 Teaching yourself

3.4.1 Self-teaching summary

3.5 Making the choice

3.6 Interview with Julia Silge on her transition to data science

3.6.1 Before becoming a data scientist you worked in academia; did the skills from your time there help you as a data scientist?

3.6.2 When deciding to become a data scientist, what did you use to pick up new skills?

3.6.3 Were any online courses particularly helpful for you?

3.6.4 Did you know going into data science what kind of work you wanted to be doing?

3.6.5 What would you recommend to people looking to get the skills to be a data scientist?

3.7 Summary

4 Building a Portfolio

4.1 Creating a project

4.1.1 Finding the data and asking a question

4.1.2 Choosing a direction

4.1.3 Filling out a GitHub README

4.2 Starting a Blog

4.2.1 Potential topics

4.2.2 Logistics

4.3 Interview with David Robinson on how he built his portfolio

4.3.1 How did you start blogging?

4.3.2 Are there any specific opportunities you have gotten from public work?

4.3.3 Are there people you think would especially benefit from doing public work?

4.3.4 How has your view on the value of public work changed over time?

4.3.5 How do you come up with ideas for your data analysis posts?

4.3.6 What’s your final piece of advice for aspiring and junior data scientists?

4.4 Summary

Part 2: Finding your Data Science Job

5 The Search: Identifying the Right Job for You

5.1 Finding jobs

5.1.1 Decoding descriptions

5.1.2 Watching for Red Flags

5.1.3 Setting your expectations

5.1.4 Leveraging your network

5.2 Deciding which jobs to apply for

5.2.1 Researching the company online

5.3 Interview with Jesse Mostipak on finding a data science job

5.3.1 What is your current role?

5.3.3 What role can and should your network play in your job search process?

5.3.4 How can you build your network?

5.3.5 What do you do if you don’t feel confident applying to data science jobs?

5.3.6 What would you say to someone who is reading job postings and thinks, “I don’t meet the full list of any job’s required qualifications?”

5.3.7 What should people think about when applying?

5.3.8 What’s your final piece of advice to aspiring data scientists?

5.4 Summary

6 The application: resumes 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 How to work with stakeholders

Part 4: Growing in your Data Science Role

13 When your data science project fails

14 Becoming a part of the data science community

15 Leaving a job gracefully

16 Moving up the corporate ladder

Appendixes

Appendix B: Example interview questions

About the Technology

Harvard Business Review called data science “the sexiest job of the 21st century.” From analyzing drug trials to helping sports teams pick new draftees, data scientists utilize data to tackle the big questions of a business. But despite demand, high competition and big expectations make data science a challenging field for the unprepared to break into and navigate. Alongside their technical skills, the successful data scientist needs to be a master of understanding data projects, adapting to company needs, and managing stakeholders.

About the book

Build Your Career in Data Science is your guide to getting your first data science job, then quickly becoming a senior employee. Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.

What's inside

  • Creating a portfolio to show off your data science projects
  • Picking the role that’s right for you
  • Assessing and negotiating an offer
  • Leaving gracefully and moving up the ladder
  • Interviews with professional data scientists about their experiences

About the reader

This book is for readers who possess the foundational technical skills of data science, and want to leverage them into a new or better job in the field.

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

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