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by Emily Robinson, Jacqueline Nolis
Build a Career in Data Science
Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About This Book
About the Authors
About the Cover Illustration
Part 1. Getting started with data science
Chapter 1. What is data science?
1.1. What is data science?
1.2. Different types of data science jobs
1.3. Choosing your path
1.4. Interview with Robert Chang, data scientist at Airbnb
Summary
Chapter 2. Data science companies
2.1. MTC: Massive Tech Company
2.2. HandbagLOVE: The established retailer
2.3. Seg-Metra: The early-stage startup
2.4. Videory: The late-stage, successful tech startup
2.5. Global Aerospace Dynamics: The giant government contractor
2.6. Putting it all together
2.7. Interview with Randy Au, quantitative user experience researcher at Google
Summary
Chapter 3. Getting the skills
3.1. Earning a data science degree
3.2. Going through a bootcamp
3.3. Getting data science work within your company
3.4. Teaching yourself
3.5. Making the choice
3.6. Interview with Julia Silge, data scientist and software engineer at RStudio
Summary
Chapter 4. Building a portfolio
4.1. Creating a project
4.2. Starting a blog
4.3. Working on example projects
4.4. Interview with David Robinson, data scientist
Summary
Chapters 1–4 resources
Part 2. Finding your data science job
Chapter 5. The search: Identifying the right job for you
5.1. Finding jobs
5.2. Deciding which jobs to apply for
5.3. Interview with Jesse Mostipak, developer advocate at Kaggle
Summary
Chapter 6. The application: Résumés and cover letters
6.1. Résumé: The basics
6.2. Cover letters: The basics
6.3. Tailoring
6.4. Referrals
6.5. Interview with Kristen Kehrer, data science instructor and course creator
Summary
Chapter 7. The interview: What to expect and how to handle it
7.1. What do companies want?
7.2. Step 1: The initial phone screen interview
7.3. Step 2: The on-site interview
7.4. Step 3: The case study
7.5. Step 4: The final interview
7.6. The offer
7.7. Interview with Ryan Williams, senior decision scientist at Starbucks
Summary
Chapter 8. The offer: Knowing what to accept
8.1. The process
8.2. Receiving the offer
8.3. Negotiation
8.4. Negotiation tactics
8.5. How to choose between two “good” job offers
8.6. Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
Summary
Chapter 5–8 resources
Part 3. Settling into data science
Chapter 9. The first months on the job
9.1. The first month
9.2. Becoming productive
9.3. If you’re the first data scientist
9.4. When the job isn’t what was promised
9.5. Interview with Jarvis Miller, data scientist at Spotify
Summary
Chapter 10. Making an effective analysis
10.1. The request
10.2. The analysis plan
10.3. Doing the analysis
10.4. Wrapping it up
10.5. Interview with Hilary Parker, data scientist at Stitch Fix
Summary
Chapter 11. Deploying a model into production
11.1. What is deploying to production, anyway?
11.2. Making the production system
11.3. Keeping the system running
11.4. Wrapping up
11.5. Interview with Heather Nolis, machine learning engineer at T-Mobile
Summary
Chapter 12. Working with stakeholders
12.1. Types of stakeholders
12.2. Working with stakeholders
12.3. Prioritizing work
12.4. Concluding remarks
12.5. Interview with Sade Snowden-Akintunde, data scientist at Etsy
Summary
Chapters 9–12 resources
Part 4. Growing in your data science role
Chapter 13. When your data science project fails
13.1. Why data science projects fail
13.2. Managing risk
13.3. What you can do when your projects fail
13.4. Interview with Michelle Keim, head of data science and machine le- earning at Pluralsight
Summary
Chapter 14. Joining the data science community
14.1. Growing your portfolio
14.2. Attending conferences
14.3. Giving talks
14.4. Contributing to open source
14.5. Recognizing and avoiding burnout
14.6. Interview with Renee Teate, director of data science at HelioCampus
Summary
Chapter 15. Leaving your job gracefully
15.1. Deciding to leave
15.2. How the job search differs after your first job
15.3. Finding a new job while employed
15.4. Giving notice
15.5. Interview with Amanda Casari, engineering manager at Google
Summary
Chapter 16. Moving up the ladder
16.1. The management track
16.2. Principal data scientist track
16.3. Switching to independent consulting
16.4. Choosing your path
16.5. Interview with Angela Bassa, head of data science, data engineeri- ing, and machine learning at iRobot
Summary
Chapters 13–16 resources
Blogs
Epilogue
Appendix. Interview questions
A.1. Coding and software development
A.2. SQL and databases
A.3. Statistics and machine learning
A.4. Behavioral
A.5. Brain teasers
Index
List of Figures
List of Tables
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List of Figures
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Chapter 2. Data science companies
Table 2.1. A summary of companies that hire data scientists
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