Build a Career in Data Science video edition

Video description

Full of useful advice, real-case scenarios, and contributions from professionals in the industry.
Sebastián Palma Mardones, ArchDaily

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.

about the technology

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.

about the book

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.

what's inside

  • Creating a portfolio of data science projects
  • Assessing and negotiating an offer
  • Leaving gracefully and moving up the ladder
  • Interviews with professional data scientists

about the audience

For learners who want to begin or advance a data science career.

about the authors

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!
Gustavo Gomes, Brightcove

Insightful overview of all aspects of a data science career.
Krzysztof Jędrzejewski, Pearson

Highly recommended.
Hagai Luger, Clarizen

NARRATED BY JEROMY LLOYD

Table of contents

  1. PART 1
  2. Chapter 1. What is data science?
  3. Chapter 1. Databases/programming
  4. Chapter 1. Different types of data science jobs
  5. Chapter 1. Choosing your path
  6. Chapter 2. Data science companies
  7. Chapter 2. HandbagLOVE: The established retailer
  8. Chapter 2. Seg-Metra: The early-stage startup
  9. Chapter 2. Videory: The late-stage, successful tech startup
  10. Chapter 2. Global Aerospace Dynamics: The giant government contractor
  11. Chapter 2. Putting it all together
  12. Chapter 3. Getting the skills
  13. Chapter 3. Choosing the school
  14. Chapter 3. Getting into an academic program
  15. Chapter 3. Going through a bootcamp
  16. Chapter 3. Getting data science work within your company
  17. Chapter 3. Teaching yourself
  18. Chapter 3. Interview with Julia Silge, data scientist and software engineer at RStudio
  19. Chapter 4. Building a portfolio
  20. Chapter 4. Choosing a direction
  21. Chapter 4. Starting a blog
  22. Chapter 4. Working on example projects
  23. Chapter 4. Interview with David Robinson, data scientist
  24. PART 2
  25. Chapter 5. The search: Identifying the right job for you
  26. Chapter 5. Decoding descriptions
  27. Chapter 5. Attending meetups
  28. Chapter 5. Deciding which jobs to apply for
  29. Chapter 6. The application: Résumés and cover letters
  30. Chapter 6. Structure
  31. Chapter 6. Deeper into the experience section: generating content
  32. Chapter 6. Cover letters: The basics
  33. Chapter 6. Referrals
  34. Chapter 7. The interview: What to expect and how to handle it
  35. Chapter 7. Step 1: The initial phone screen interview
  36. Chapter 7. Step 2: The on-site interview
  37. Chapter 7. The technical interview
  38. Chapter 7. The behavioral interview
  39. Chapter 7. Step 3: The case study
  40. Chapter 7. The offer
  41. Chapter 8. The offer: Knowing what to accept
  42. Chapter 8. Negotiation
  43. Chapter 8. How much you can negotiate
  44. Chapter 8. Negotiation tactics
  45. Chapter 8. Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
  46. PART 3
  47. Chapter 9. The first months on the job
  48. Chapter 9. Understanding and setting expectations
  49. Chapter 9. Knowing your data
  50. Chapter 9. Becoming productive
  51. Chapter 9. Building relationships
  52. Chapter 9. If you’re the first data scientist
  53. Chapter 9. The work environment is toxic
  54. Chapter 9. Interview with Jarvis Miller, data scientist at Spotify
  55. Chapter 10. Making an effective analysis
  56. Chapter 10. The request
  57. Chapter 10. Doing the analysis
  58. Chapter 10. Important points for exploring and modeling
  59. Chapter 10. Wrapping it up
  60. Chapter 11. Deploying a model into production
  61. Chapter 11. Making the production system
  62. Chapter 11. Building an API
  63. Chapter 11. Deploying an API
  64. Chapter 11. Keeping the system running
  65. Chapter 12. Working with stakeholders
  66. Chapter 12. Working with stakeholders
  67. Chapter 12. Communicating constantly
  68. Chapter 12. Prioritizing work
  69. Chapter 12. Concluding remarks
  70. PART 4
  71. Chapter 13. When your data science project fails
  72. Chapter 13. The data doesn’t have a signal
  73. Chapter 13. Managing risk
  74. Chapter 13. Interview with Michelle Keim, head of data science and machine le- earning at Pluralsight
  75. Chapter 14. Joining the data science community
  76. Chapter 14. Attending conferences
  77. Chapter 14. Giving talks
  78. Chapter 14. Contributing to open source
  79. Chapter 14. Recognizing and avoiding burnout
  80. Chapter 15. Leaving your job gracefully
  81. Chapter 15. How the job search differs after your first job
  82. Chapter 15. Finding a new job while employed
  83. Chapter 15. Giving notice
  84. Chapter 15. Interview with Amanda Casari, engineering manager at Google
  85. Chapter 16. Moving up the ladder
  86. Chapter 16. The management track
  87. Chapter 16. Principal data scientist track
  88. Chapter 16. Switching to independent consulting
  89. Chapter 16. Choosing your path
  90. Epilogue
  91. Appendix. Interview questions - A.1. Coding and software development
  92. Appendix. Interview questions - A.1.5. Frequently used package/library
  93. Appendix. Interview questions - A.2. SQL and databases
  94. Appendix. Interview questions - A.3. Statistics and machine learning
  95. Appendix. Interview questions - A.3.7. Training vs. test data
  96. Appendix. Interview questions - A.4. Behavioral
  97. Appendix. Interview questions - A.5. Brain teasers

Product information

  • Title: Build a Career in Data Science video edition
  • Author(s): Emily Robinson, Jacqueline Nolis
  • Release date: March 2020
  • Publisher(s): Manning Publications
  • ISBN: None