Technical Literacy: A Practical Guide for Product Managers
Product excellence requires tech literacy. Here is how to level up your skills.
I have preached this more than once, technical literacy for product managers is essential. In the area of AI, it is critical for everyone, but product managers have to stay ahead of the curve.
Today I will write about gaining technical literacy (free courses only). If you dont have the self-discipline required for self-education (and many of us dont), please pay for your education — it will keep you accountable.
A few assumptions:
- I assume you have zero technical background and start from scratch.
- Learning syntax is not necessary anymore, as AI is here to help in any coding exercise. But, you have to understand what you are asking for and be able to read code to check the results you get.
- You are willing to invest 3–4 hours weekly to level up.
Understand the Basics of Coding and Systems Design:
You don’t need to be a programmer, but you should understand coding. Try Python or JavaScript, which are friendly for beginners. Use resources like Codecademy, Coursera, or edX to help you:
- Python course: https://www.coursera.org/learn/python-crash-course
- Data analysis with Python: https://www.coursera.org/learn/data-analysis-with-python
Also, learn about systems design principles, including how different system components interact, data flow, system performance, etc. learn Data Bases and be able to query them by yourself using SQL:
- SQL for Data Science: https://www.coursera.org/learn/sql-data-science
Please notice there are tools text-to-SQL that allow uploading your diagram and getting the SQL queries; I suggest using them while you learn. If you take only one advice form this article it’s the SQL part — being able to query your data is a superpower for Product Managers.
Master the Technologies Relevant to Your Product:
As a product manager, you should have a strong understanding of the technology stack that your product is built on. This could involve web technologies (like HTML, CSS, JavaScript), database systems (SQL, NoSQL), cloud services (AWS, Google Cloud, Azure), mobile technologies (iOS, Android), and so on. Nowadays, in many instances, it also means mastering AI and big data methodologies, and now that you gained some Python and SQL experience, it's time to dive deep into AI.
While AI is a vast topic, I suggest you start with the classic courses of Andrew ng (the godfather of AI):
- Introduction to Machine Learning: https://www.coursera.org/specializations/machine-learning-introduction
And move up from there according to your organization’s needs and personal taste.
If you have any questions, leave them in the comments, and I will gladly help you with your journey.
