book

Build a Career in Data Science

by Emily Robinson, Jacqueline Nolis

March 2020

Beginner

354 pages

13h 34m

English

Manning Publications

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1.1. What is data science?1.2. Different types of data science jobs1.3. Choosing your path1.4. Interview with Robert Chang, data scientist at AirbnbSummary

2.1. MTC: Massive Tech Company2.2. HandbagLOVE: The established retailer2.3. Seg-Metra: The early-stage startup2.4. Videory: The late-stage, successful tech startup2.5. Global Aerospace Dynamics: The giant government contractor2.6. Putting it all together2.7. Interview with Randy Au, quantitative user experience researcher at GoogleSummary
3.1. Earning a data science degree3.2. Going through a bootcamp3.3. Getting data science work within your company3.4. Teaching yourself3.5. Making the choice3.6. Interview with Julia Silge, data scientist and software engineer at RStudioSummary
4.1. Creating a project4.2. Starting a blog4.3. Working on example projects4.4. Interview with David Robinson, data scientistSummaryChapters 1–4 resources
5.1. Finding jobs5.2. Deciding which jobs to apply for5.3. Interview with Jesse Mostipak, developer advocate at KaggleSummary
6.1. Résumé: The basics6.2. Cover letters: The basics6.3. Tailoring6.4. Referrals6.5. Interview with Kristen Kehrer, data science instructor and course creatorSummary
7.1. What do companies want?7.2. Step 1: The initial phone screen interview7.3. Step 2: The on-site interview7.4. Step 3: The case study7.5. Step 4: The final interview7.6. The offer7.7. Interview with Ryan Williams, senior decision scientist at StarbucksSummary
8.1. The process8.2. Receiving the offer8.3. Negotiation8.4. Negotiation tactics8.5. How to choose between two “good” job offers8.6. Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLUSummaryChapter 5–8 resources
9.1. The first month9.2. Becoming productive9.3. If you’re the first data scientist9.4. When the job isn’t what was promised9.5. Interview with Jarvis Miller, data scientist at SpotifySummary
10.1. The request10.2. The analysis plan10.3. Doing the analysis10.4. Wrapping it up10.5. Interview with Hilary Parker, data scientist at Stitch FixSummary
11.1. What is deploying to production, anyway?11.2. Making the production system11.3. Keeping the system running11.4. Wrapping up11.5. Interview with Heather Nolis, machine learning engineer at T-MobileSummary
12.1. Types of stakeholders12.2. Working with stakeholders12.3. Prioritizing work12.4. Concluding remarks12.5. Interview with Sade Snowden-Akintunde, data scientist at EtsySummaryChapters 9–12 resources
13.1. Why data science projects fail13.2. Managing risk13.3. What you can do when your projects fail13.4. Interview with Michelle Keim, head of data science and machine le- earning at PluralsightSummary
14.1. Growing your portfolio14.2. Attending conferences14.3. Giving talks14.4. Contributing to open source14.5. Recognizing and avoiding burnout14.6. Interview with Renee Teate, director of data science at HelioCampusSummary
15.1. Deciding to leave15.2. How the job search differs after your first job15.3. Finding a new job while employed15.4. Giving notice15.5. Interview with Amanda Casari, engineering manager at GoogleSummary
16.1. The management track16.2. Principal data scientist track16.3. Switching to independent consulting16.4. Choosing your path16.5. Interview with Angela Bassa, head of data science, data engineeri- ing, and machine learning at iRobotSummaryChapters 13–16 resourcesBlogs

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About This Book

Build a Career in Data Science was written to help you enter the field of data science and grow your career in it. It walks you through the role of a data scientist, how to get the skills you need, and the steps to getting a data science job. After you have a job, this book helps you understand how to mature in the role and eventually become a larger part of the data science community, as well as a senior data scientist. After reading this book, you should be confident about how to advance your career.

Who should read this book

This book is for people who have not yet entered the field of data science but are considering it, as well as people who are in the first few years of the role. Aspiring data scientists will learn the ...