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If you enrolled prior to 2026, you will continue your studies under the existing "Digital Culture" module program. A brief description of the courses is provided below.
| Section | Contents |
| Module description | This module allows students to develop basic digital skills to solve professional problems. This course will teach you key principles of working with data and how to apply them to solve practical problems, as well as master relevant tools and technologies for comfortable use in the digital environment. |
| How to sign up for the course |
Step 1. Activate your account on the lms.itmo.ru. An activation link will be sent to the email address listed on your personal ISU page on the day the course starts. Step 2. Open the course materials and start learning. A more detailed instruction is here (in Russian). |
| Contact us |
Address:
Phone: +7 (812) 607-04-64 E-mail: aic@itmo.ru |
Introduction to Digital Culture and Computer Programming
Core component: computer architecture and operating systems, programming technologies, network technologies, information security, embedded systems, bibliographic research.
Elective component: digital ethics, Internet of Things, blockchain, VR/AR/MR, digital humanities, social networks, as well as a Python course for beginners.
Course workload:
Course language: Russian
Learning format: Blended learning: the lectures and assignments take place online, while the seminars and workshops are held on campus
Assessment format: Students are assessed based on their completion of online assignments
Data Storage and Processing
Three modules: an introduction to data processing and analysis (visualization, exploratory analysis, time series), database fundamentals (relational model, SQL), and NoSQL systems (key-value, document, columnar, and graph stores). The advanced level covers custom storage design and transaction management.
Course workload:
Course language: Russian
Learning format: Blended learning: the lectures and assignments take place online, while the seminars and workshops are held on campus
Assessment format: Students are assessed based on their completion of online assignments
Applied Statistics
Fundamentals of probability theory (probability space, random variables and their distributions, expected value, variance) and applied statistics (point estimates, confidence intervals, hypothesis testing). At an advanced level — modeling in Python (scipy.stats, numpy).
Course workload:
Course language: Russian
Learning format: Blended learning: the lectures and assignments take place online, while the seminars and workshops are held on campus
Assessment format: Students are assessed based on their completion of online assignments
Machine Learning
Classical approaches to regression, classification, and clustering tasks. Overfitting, regularization, feature engineering. Linear and logistic regression, kNN, SVM, K-means, decision trees, ensembles, dimensionality reduction methods, unsupervised learning.
Course workload:
Course language: Russian
Learning format: Blended learning: the lectures and assignments take place online, while the seminars and workshops are held on campus
Assessment format: Students are assessed based on their completion of online assignments
