Table of Contents
1. Getting started with data science
Chapter 1. What is data science?
1.2. Different types of data science jobs
1.4. Interview with Robert Chang, data scientist at Airbnb
What was your first data science journey?
Chapter 2. Data science companies
2.1. MTC: Massive Tech Company
2.1.1. Your team: One of many in MTC
2.2. HandbagLOVE: The established retailer
2.2.1. Your team: A small group struggling to grow
2.3. Seg-Metra: The early-stage startup
2.3.2. The tech: Cutting-edge technology that’s taped together
2.4. Videory: The late-stage, successful tech startup
2.4.1. The team: Specialized but with room to move around
2.4.2. The tech: Trying to avoid getting bogged down by legacy code
2.5. Global Aerospace Dynamics: The giant government contractor
2.5.1. The team: A data scientist in a sea of engineers
2.7. Interview with Randy Au, quantitative user experience researcher at Google
Are there big differences between large and small companies?
Are there differences based on the industry of the company?
What’s your final piece of advice for beginning data scientists?
3.1. Earning a data science degree
3.3. Getting data science work within your company
3.6. Interview with Julia Silge, data scientist and software engineer at RStudio
When deciding to become a data scientist, what did you use to pick up new skills?
Did you know going into data science what kind of work you wanted to be doing?
What would you recommend to people looking to get the skills to be a data scientist?
Chapter 4. Building a portfolio
4.3. Working on example projects
4.3.1. Data science freelancers
4.3.2. Training a neural network on offensive license plates
4.4. Interview with David Robinson, data scientist
Are there any specific opportunities you have gotten from public work?
Are there people you think would especially benefit from doing public work?
How has your view on the value of public work changed over time?
How do you come up with ideas for your data analysis posts?
What’s your final piece of advice for aspiring and junior data scientists?
2. Finding your data science job
Chapter 5. The search: Identifying the right job for you
5.2. Deciding which jobs to apply for
5.3. Interview with Jesse Mostipak, developer advocate at Kaggle
What recommendations do you have for starting a job search?
How can you build your network?
What do you do if you don’t feel confident applying to data science jobs?
What’s your final piece of advice to aspiring data scientists?
Chapter 6. The application: Résumés and cover letters
6.1.2. Deeper into the experience section: generating content
6.2. Cover letters: The basics
6.5. Interview with Kristen Kehrer, data science instructor and course creator
How many times would you estimate you’ve edited your résumé?
What are common mistakes you see people make?
Do you tailor your résumé to the position you’re applying to?
What strategies do you recommend for describing jobs on a résumé?
What’s your final piece of advice for aspiring data scientists?
Chapter 7. The interview: What to expect and how to handle it
7.2. Step 1: The initial phone screen interview
7.3. Step 2: The on-site interview
7.5. Step 4: The final interview
7.7. Interview with Ryan Williams, senior decision scientist at Starbucks
What are the things you need to do to knock an interview out of the park?
How do you handle the times where you don’t know the answer?
What should you do if you get a negative response to your answer?
What has running interviews taught you about being an interviewee?
Chapter 8. The offer: Knowing what to accept
8.5. How to choose between two “good” job offers
8.6. Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
What should you consider besides salary when you’re considering an offer?
What are some ways you prepare to negotiate?
What do you do if you have one offer but are still waiting on another one?
What’s your final piece of advice for aspiring and junior data scientists?
Chapter 9. The first months on the job
9.1.1. Onboarding at a large organization: A well-oiled machine
9.1.2. Onboarding at a small company: What onboarding?
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
What were some things that surprised you in your first data science job?
What are some issues you faced?
Can you tell us about one of your first projects?
What would be your biggest piece of advice for the first few months?
Chapter 10. Making an effective analysis
10.3.1. Importing and cleaning data
10.5. Interview with Hilary Parker, data scientist at Stitch Fix
How does thinking about other people help your analysis?
How do you structure your analyses?
What kind of polish do you do in the final version?
How do you handle people asking for adjustments to an analysis?
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
11.5. Interview with Heather Nolis, machine learning engineer at T-Mobile
What does “machine learning engineer” mean on your team?
What was it like to deploy your first piece of code?
If you have things go wrong in production, what happens?
What’s your final piece of advice for data scientists working with engineers?
Chapter 12. Working with stakeholders
12.2. Working with stakeholders
12.2.1. Understanding the stakeholder’s goals
12.3.1. Both innovative and impactful work
12.3.2. Not innovative but still impactful work
12.5. Interview with Sade Snowden-Akintunde, data scientist at Etsy
Why is managing stakeholders important?
How did you learn to manage stakeholders?
Was there a time where you had difficulty with a stakeholder?
What do junior data scientists frequently get wrong?
Do you always try to explain the technical part of the data science?
What’s your final piece of advice for junior or aspiring data scientists?
4. Growing in your data science role
Chapter 13. When your data science project fails
13.1. Why data science projects fail
13.1.1. The data isn’t what you wanted
13.3. What you can do when your projects fail
13.4. Interview with Michelle Keim, head of data science and machine learning at Pluralsight
When was a time you experienced a failure in your career?
Are there red flags you can see before a project starts?
How does the way a failure is handled differ between companies?
Chapter 14. Joining the data science community
14.4. Contributing to open source
14.5. Recognizing and avoiding burnout
14.6. Interview with Renee Teate, director of data science at HelioCampus
What are the main benefits of being on social media?
What would you say to people who say they don’t have the time to engage with the community?
Is there value in producing only a small amount of content?
Were you worried the first time you published a blog post or gave a talk?
Chapter 15. Leaving your job gracefully
15.2. How the job search differs after your first job
15.3. Finding a new job while employed
15.5. Interview with Amanda Casari, engineering manager at Google
How do you know it’s time to start looking for a new job?
Have you ever started a job search and decided to stay instead?
Do you see people staying in the same job for too long?
Can you change jobs too quickly?
What’s your final piece of advice for aspiring and new data scientists?
Chapter 16. Moving up the ladder
16.1.1. Benefits of being a manager
16.2. Principal data scientist track
16.2.1. Benefits of being a principal data scientist
16.3. Switching to independent consulting
16.3.1. Benefits of independent consulting
What’s the day-to-day life as a manager like?
What are the signs you should move on from being an independent contributor?
Do you have to eventually transition out of being an independent contributor?
What advice do you have for someone who wants to be a technical lead but isn’t quite ready for it?
What’s your final piece of advice to aspiring and junior data scientist?
A.1. Coding and software development
A.1.2. Tell whether a number is prime
A.1.5. Frequently used package/library
A.1.6. R Markdown or Jupyter Notebooks
A.1.7. When should you write functions or packages/libraries?
A.3. Statistics and machine learning
A.3.3. Explain a confusion matrix
A.3.4. Interpreting regression models
A.4.1. Project that had the most impact
A.4.3. Previous job reflections
A.4.4. Senior person making a mistake based on data