Data analysis in applied sciences

Applying data science in areas not directly related to IT, from high-energy physics to industrial design of medicines
Who will benefit from this course
Professionals who want to apply data science in fields not directly related to IT.
What you’ll learn
How to translate an informal scientific or industrial task into the language of data science. It is not easy to learn this by listening to lectures, so the second year of study is devoted entirely to independent practical research.
How you’ll apply it
In the modern world, data analysis tasks arise in practically any project, from forecasting stock quotes to placing oil rigs.


The main distinguishing feature of the Data Analysis in Applied Sciences course is that students spend most of the second year working on applied research projects. Final assessment is largely determined by the quality of this project.

Students working on their Bachelor’s or Master’s theses in parallel with studying at the School of Data Analysis can use their project as the basis of their university work.

First semester
Recovery of functional laws from empirical evidence
Fundamentals of stochastics. Stochastic models
Algorithms and data structures, part 1
Second semester
Machine learning, part 1
Fundamentals of statistics in machine learning
Third semester
Convex analysis and optimization
Machine learning, part 2
Project work
Fourth semester
Deep learning
Project work