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.
What are the keys …
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.
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.
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.
For learners who want to begin or advance a data science career.
Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor.
The perfect companion for someone who wants to be a successful data scientist!PART 1
Chapter 1. What is data science?
Chapter 1. Databases/programming
Chapter 1. Different types of data science jobs
Chapter 1. Choosing your path
Chapter 2. Data science companies
Chapter 2. HandbagLOVE: The established retailer
Chapter 2. Seg-Metra: The early-stage startup
Chapter 2. Videory: The late-stage, successful tech startup
Chapter 2. Global Aerospace Dynamics: The giant government contractor
Chapter 2. Putting it all together
Chapter 3. Getting the skills
Chapter 3. Choosing the school
Chapter 3. Getting into an academic program
Chapter 3. Going through a bootcamp
Chapter 3. Getting data science work within your company
Chapter 3. Teaching yourself
Chapter 3. Interview with Julia Silge, data scientist and software engineer at RStudio
Chapter 4. Building a portfolio
Chapter 4. Choosing a direction
Chapter 4. Starting a blog
Chapter 4. Working on example projects
Chapter 4. Interview with David Robinson, data scientist
PART 2
Chapter 5. The search: Identifying the right job for you
Chapter 5. Decoding descriptions
Chapter 5. Attending meetups
Chapter 5. Deciding which jobs to apply for
Chapter 6. The application: Résumés and cover letters
Chapter 6. Structure
Chapter 6. Deeper into the experience section: generating content
Chapter 6. Cover letters: The basics
Chapter 6. Referrals
Chapter 7. The interview: What to expect and how to handle it
Chapter 7. Step 1: The initial phone screen interview
Chapter 7. Step 2: The on-site interview
Chapter 7. The technical interview
Chapter 7. The behavioral interview
Chapter 7. Step 3: The case study
Chapter 7. The offer
Chapter 8. The offer: Knowing what to accept
Chapter 8. Negotiation
Chapter 8. How much you can negotiate
Chapter 8. Negotiation tactics
Chapter 8. Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
PART 3
Chapter 9. The first months on the job
Chapter 9. Understanding and setting expectations
Chapter 9. Knowing your data
Chapter 9. Becoming productive
Chapter 9. Building relationships
Chapter 9. If you’re the first data scientist
Chapter 9. The work environment is toxic
Chapter 9. Interview with Jarvis Miller, data scientist at Spotify
Chapter 10. Making an effective analysis
Chapter 10. The request
Chapter 10. Doing the analysis
Chapter 10. Important points for exploring and modeling
Chapter 10. Wrapping it up
Chapter 11. Deploying a model into production
Chapter 11. Making the production system
Chapter 11. Building an API
Chapter 11. Deploying an API
Chapter 11. Keeping the system running
Chapter 12. Working with stakeholders
Chapter 12. Working with stakeholders
Chapter 12. Communicating constantly
Chapter 12. Prioritizing work
Chapter 12. Concluding remarks
PART 4
Chapter 13. When your data science project fails
Chapter 13. The data doesn’t have a signal
Chapter 13. Managing risk
Chapter 13. Interview with Michelle Keim, head of data science and machine le- earning at Pluralsight
Chapter 14. Joining the data science community
Chapter 14. Attending conferences
Chapter 14. Giving talks
Chapter 14. Contributing to open source
Chapter 14. Recognizing and avoiding burnout
Chapter 15. Leaving your job gracefully
Chapter 15. How the job search differs after your first job
Chapter 15. Finding a new job while employed
Chapter 15. Giving notice
Chapter 15. Interview with Amanda Casari, engineering manager at Google
Chapter 16. Moving up the ladder
Chapter 16. The management track
Chapter 16. Principal data scientist track
Chapter 16. Switching to independent consulting
Chapter 16. Choosing your path
Epilogue
Appendix. Interview questions - A.1. Coding and software development
Appendix. Interview questions - A.1.5. Frequently used package/library
Appendix. Interview questions - A.2. SQL and databases
Appendix. Interview questions - A.3. Statistics and machine learning
Appendix. Interview questions - A.3.7. Training vs. test data
Appendix. Interview questions - A.4. Behavioral
Appendix. Interview questions - A.5. Brain teasers