Digital Culture Bachelor’s courses

Section	Contents
Module description	The lectures in the Digital Culture module equip students with the competencies necessary to use the information and communication technologies that help us navigate the digital environment, as well as interact with society and find digital solutions for professional tasks. Here, you will learn to formulate problems, choose the correct tools and algorithms for data analysis, and use them. You will also be able to interpret the results that you get. More information you can find here Presentation for Bachelor’s students (in Russian)
How to sign up for the course	Step 1. Activate your account on the Open Education national platform. 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: 49 Kronverksky Avenue, room 420 14 Birzhevaya Line, rooms 446, 447 Phone: +7 (812) 607-04-64 E-mail: dc@itmo.ru

Courses:

Introduction to Digital Culture and Computer Programming

The course is divided into three parts that introduce students to the key advances in the field of ICT.

The first, fundamental, part consists of lectures on hardware and software architecture, programming technologies, network technologies, artificial intelligence, information security, quantum technologies, internet and web technologies.
The next part covers issues connected to the relationship between humans and the digital society, such as digital ethics, smart devices, the technologies of digital economics, blockchain, online etiquette, and the basics of personal information security.
The final part of the course includes lectures on the following topics: digital education, digital humanities, VR/AR/MR technologies, social networks, and bibliographical reference retrieval.

The course also includes the basics of Python. 7 lectures are mandatory for viewing (include lectures from each block), from the rest you can choose what you are most interested in.

Course workload:

3 credits
108 academic hours

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

More information (in Russian)

Data Storage and Processing

The course is meant to present the tools and technologies that are necessary in the world where we have to deal with constantly growing amounts of data. Thus, we need to be able to process, analyse, and store all of this incoming information.

Basic and Advanced levels for students enrolled in 2022 and late

The course includes the following parts:

Preliminary data processing – includes respective methods and instruments, the basic descriptive statistics, and data visualization;
Relational database management systems – covers the key components of relational databases and provides practical examples on the use of the SQL language to process such data;
NoSQL data storage systems – covers various NoSQL database types from document and key-value to tabular and graph.

Course workload:

3 credits
108 academic hours

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

More information (in Russian)

Applied Statistics

In this course, we’ll introduce our students to the basics of probability theory, uni- and multivariate random variables, and their characteristics. We will also study and observe the law of large numbers and the central limit theorem in action. Then we will move on to studying statistics, starting with sample characteristics and continuing with point estimation of the unknown parameters of the general population. We will also compare the point and interval estimation methods, explain the hypothesis verification task and cover the goodness of fit criteria.

Basic and Advanced levels for students enrolled in 2022 and late

Course workload:

2 credits
72 academic hours

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

The course covers the main machine learning methods (supervised and unsupervised, reinforcement learning) and the problems they can be applied to. One of the key methods for supervised learning is regression (linear, multivariate, polynomial, logistic), which we will study in detail in the course. We will then move on to classification (naive Bayes classifier and the k-nearest neighbors algorithm) and clusterization (hierarchical clustering and k-means clustering) methods. We will also delve into factor analysis as a method of lowering the sample dimension. For the final part, we will study decision trees and the methods of statistical model evaluation, as well as reinforcement learning methods.

Basic and Advanced levels for students enrolled in 2022 and later

Course workload: