Course list

Digital literacy courses are intended to form the basic competencies needed to safely and efficiently use digital technologies and Internet resources in academia. 

The syllabus includes both common topics (from working with text editors and bibliographic sources to computer security and legal aspects of working with information) and specialized ones — for example corpus technologies, geographic information systems, introduction to Digital Humanities, digital preservation of cultural heritage and etc. 

The Digital Literacy course is usually taught in a blended learning format and has a module structure with an abundant amount of components, several of which may be customized for different areas of instruction. Common course modules are set to provide necessary skills regardless of the field of study. At the programme level, modules are selected with focus on learning those skills considered relevant to a given programme’s field. For students pursuing individual educational trajectories, modules shall be determined in which each learner is personally interested.

The Python programming language is widely used not only by programmers, being as important a tool as Excel or PowerPoint today. Its simplicity and rich functionality allows people of almost any profession to solve their applied problems easier and more efficiently. 

As a rule, no prior knowledge of programming is required to complete the courses as these are specially created for students of non-engineering specializations and people from outside the IT world. They help to master the basics of programming, sufficient for further use in professional activities. 

The purpose of the Data Analysis courses is to provide students with the basic tools to be able to critically analyze research results, as well as describe, visualize and present such information. 

As a rule, taking courses at beginner / basic levels, students get acquainted with the basic concepts of statistics (sampling, distribution, measure of central tendency and dispersion, correlation), understand when and what conclusions can be drawn based on certain data. Students also learn to work directly with data: load and process data, fill in gaps, read descriptive statistics, etc. Part of the program is devoted to data visualization: students learn how our brain perceives visual information, how to make visualizations more effective and avoid information distortion. 

Courses are devoted to the analysis of machine learning algorithms: from linear regression to algorithm compositions. These also include working with data: cleaning, normalizing, removing outliers. During the training, students solve both theoretical problems on paper and homework using Python, and participate in Data Science competitions. 

Machine learning courses are usually taught at programmes which set the advanced level of digital  skills (or optional for the basic level). The prerequisites for such courses are knowledge of the basics of higher mathematics (mathematical analysis, linear algebra, probability theory and the basics of mathematical statistics) and at least intermediate knowledge of Python.

The curricula of educational programmes also include courses that form subject-oriented digital skills. For example: 

Processing and Analysis of Physical Experiment Data for students of the 'Physics' programme, Data Analysis in Politics and Journalism for students of the "International Relations" programme who chose the Journalism and Global Media concentration, "Basics of Web Layout and Website Creation" for students enrolled in "Design and Promotion of a Digital Product" at the Design School, etc.