How to Build Your Career in Ai 1

“AI is the new electricity. It will transform and improve all areas of human life.”

Andrew Ng

Table of Contents

  • Introduction: Coding AI is the New Literacy.
  • Chapter 1: Three Steps to Career Growth.
  • Chapter 2: Learning Technical Skills for a Promising AI Career.


  • Chapter 3: Should You Learn Math to Get a Job in AI?
  • Chapter 4: Scoping Successful AI Projects.
  • Chapter 5: Finding Projects that Complement Your Career Goals.


  • Chapter 6: Building a Portfolio of Projects that Shows Skill Progression.
  • Chapter 7: A Simple Framework for Starting Your AI Job Search.


  • Chapter 8: Using Informational Interviews to Find the Right Job.
  • Chapter 9: Finding the Right AI Job for You.
  • Chapter 10: Keys to Building a Career in AI.
  • Chapter 11: Overcoming Imposter Syndrome.
  • Final Thoughts: Make Every Day Count.


Coding AI Is the New Literacy

Today we take it for granted that many people know how to read and write. Someday, I hope, it will be just as common that people know how to write code, specifically for AI.

Several hundred years ago, society didn’t view language literacy as a necessary skill. A small number of people learned to read and write, and everyone else let them do the reading and writing. It took centuries for literacy to spread, and now society is far richer for it.

Words enable deep human-to-human communication. Code is the deepest form of human-to-machine communication. As machines become more central to daily life, that communication becomes ever more important.

Traditional software engineering — writing programs that explicitly tell a computer sequences of steps to execute — has been the main path to code literacy. Many introductory programming classes use creating a video game or building a website as examples. But AI, machine learning, and data science offer a new paradigm in which computers extract knowledge from data. This technology offers an even better pathway to coding.

Many Sundays, I buy a slice of pizza from my neighborhood pizza parlor. The gentleman behind the counter has little reason to learn how to build a video game or write his own website software (beyond personal growth and the pleasure of gaining a new skill).

But AI and data science have great value even for a pizza maker. A linear regression model might enable him to better estimate demand so he can optimize the restaurant’s staffing and supply chain. He could better predict sales of Hawaiian pizza — my favorite! — so he could make more Hawaiian pies in advance and reduce the amount of time customers had to wait for them.

Uses of AI and data science can be found in almost any situation that produces data. Thus, a wide variety of professions will find more uses for custom AI applications and data-derived insights than for traditional software engineering. This makes literacy in AI-oriented coding even more valuable than traditional coding. It could enable countless individuals to harness data to make their lives richer.

I hope the promise of building basic AI applications, even more than that of building basic traditional software, encourages more people to learn how to code. If society embraces this new form of literacy as it has the ability to read and write, we will all benefit.