Overview

This course is being read from the conception that the Data Scientific pipeline that makes ML magic, which is currently more known as AI is the only important part of any project. All other technical parts are basically inessential for competition.

This fall the vision of the course aligned with the reality pretty well. Using tools like lovable.dev or cursor one can create and deploy anything that doesn’t require DS knowledge in the matter of hours. For these tools we will have a relatively short but comprehensive overview, that will allow participants to employ all powers of generative coding.

The course will cover main techniques and principles of organizing DS work with python, key libraries, design principles and ML-Ops practices.

Primitives of data science will be taught in old style and tested in old style of oral examination of ability to use them without generative AI with minimum usage of documentation, the same way as it being done for courses in maths with slight correction for AI age – some of your examinators will be robots.

To solve real tasks and for capstone project development usage of generative AI will be strongly encouraged.

At the end of the course most of the participants will have their own DS-based services, most probably internal for harbour space institute of technology ready to gain first users.