I am joining the TD School lecturer team — teaching a course on the practical use of artificial intelligence.
My module focuses on working with data. I spent most of my career in analytics, so this is where I can speak without unnecessary theory and with concrete examples.
We will cover:
- analyzing large datasets;
- finding patterns in data;
- automating routine tasks;
- preparing reports and working with spreadsheets;
- testing hypotheses and accelerating analytical processes.
Everything based on real examples that can be applied right away.
A common question: is it worth learning AI if everything changes so quickly? I think yes — but the value today is not in knowing a specific service or feature. Tools change constantly. What stays relevant is understanding how language models work, which tasks to trust them with, and where to verify results — regardless of what's popular this quarter.
The course will not promise to automate your entire business in a week. Instead: honest scenarios — where AI genuinely saves time, and where a human is still irreplaceable.
