Start with the "Fundamentals" simulator; it is the foundation. You'll understand your role, how to lead a team, plan, set OKRs and a vision, understand JTBD, choose an MVP, and calculate unit economics.
Start with the "Fundamentals" simulator; it is the foundation. You'll understand your role, how to lead a team, plan, set OKRs and a vision, understand JTBD, choose an MVP, and calculate unit economics.
Add the necessary minimum to the Fundamentals. First, "The Fundamentals of Tech"—to be able to speak the same language with the team and launch what’s promised.
Secondly, add ML Foundations that will give you the necessary foundation for Machine Learning, what business problems it solves well, and how to spot them in your product.
Finally, Basics of Analytics will help you understand and deal with metrics and conversions, as well as learn to work with a popular BI tool (Mixpanel) using real data.
To grow quickly, it's important to deliver value to the company and launch what you've promised. Start with the technical foundation, as 99% of products today rely on services, and a PM must understand them and speak the same language as the team.
Which product manager will succeed: one who acts "as they see it" or one who is guided by data and metrics? This is precisely what this simulator is about.
Sometimes, new product managers have particular skill sets but lack a system vision, and thus might miss the big picture. If you feel like you need to structure your knowledge, we recommend taking the "Fundamentals" course as an option.
Every company is different, so research what skills are essential for yours and add them to your package with a 30% discount. For example, a Sr. PM at Uber should excel at A/B testing, be able to use LLMs in the product, etc.
Big companies expect you to have top-notch knowledge. Firstly, this means an excellent understanding of technology and the ability to communicate with the tech team in the same language.
Secondly, it is the ability to manage a product based on data: understanding metrics and funnels, and having practical (not theoretical) experience working with analytics systems.
Large companies grow because every step they take is scientifically verified through A/B testing. A product manager must be able to formulate hypotheses, select the right metrics, and make decisions based on each test.
Large companies have long used ML for optimization tasks such as ranking, price selection, and classification (whether to offer or not offer a certain user a coupon). 90% of all "fashionable AI" is classic ML.
Companies are now actively looking for ways to use LLMs in their products for text generation, review classification, etc. This is a very new product manager skill for solving the remaining 10% of business tasks.
Large companies expect their product team to be quick. Full-stack AI prototyping allows you to create a realistic prototype of a feature in half an hour and immediately present it to users and stakeholders.
To begin your journey into AI product development (or simply product development with AI skills), you need to understand the fundamentals of ML. Classic regression and classification algorithms solve 90% of real-world business problems.
LLMs allow you to solve the 10% of tasks where standard ML is ineffective (for example, free text analysis). Large companies are now actively competing with each other in this area of AI-first features.
Turns out, AI is an excellent coder. This allows product managers to quickly create realistic prototypes of products and features or even start their own side project. No coding required.
The courses above will be useful for all product managers. This advanced course, however, is especially relevant for those who will be leading an ML product alongside data engineers.
It's logical to follow the tech course with a course on AI vibe coding, where you'll learn how to build clickable prototypes in minutes and simple products with a backend and databases in hours. At the same time, you'll gain a better understanding of the tech world.
Add database skills to the mix. Start with simple queries, and eventually learn to do complex things with ChatGPT. Practice on a real dataset using the SuperSet tool.
A/B testing is a way to develop a product based on a scientific approach, not wild guesses. The ability to formulate the right hypothesis, select the right metric, and make a decision on the test is a distinct skill for a product manager.
Got an idea of what to build? In half an hour, turn it into a working prototype using AI coding and send a link to live users by evening. Without a line of code.