About the Project
The ability to work with data and master digital tools is essential in the 21st century. As technology continues to evolve, so does the job market. Today, professionals in every field regularly face data-related tasks: lawyers review hundreds of cases, linguists analyse thousands of texts across multiple languages, and economists build predictive models based on vast amounts of information.
There remains a shortage of specialists who are proficient in working with data, and employers highly value such employees. That is why the knowledge gained from these courses gives graduates a competitive edge when entering the job market. These skills are also invaluable in research—in the coming decades, major scientific breakthroughs are expected to occur at the intersection of information technology and other disciplines.
Our Numbers
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100 %
of undergraduate programmes are covered by the project
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>30000
students take project courses annually
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>150
courses are offered throughout the academic year
What We Teach
To ensure students acquire a sufficient level of competence, we have identified three core compulsory modules:
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Digital Literacy
We explain how a computer works—but in more detail than you'd get at school. We show you hidden tricks for managing files and teach you how to work with Excel. We explain what phishing links look like and why not every love letter in your inbox should be opened. You’ll find out why it's a bad idea to use a simple password and how to check whether a website is safe to visit. We also cover artificial intelligence: how to work with different models and how to write effective prompts.
What you'll learn: how to analyse content, spot fakes and deepfakes, manage your digital footprint, work with academic sources, and format bibliographies correctly. You'll also discover how intellectual property law and open licenses work. As an optional extra, you can dive into advanced prompt engineering techniques.
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Algorithmic Thinking and Programming
In this course, we teach the fundamentals of Python.
What you'll learn: how to write small programs and algorithms, solve practical problems, conduct research, and build your own tools. The tasks vary by field. For example, law students can analyse legal texts and predict court case outcomes, while designers can try their hand at game development. And all of this will give you a real edge when you start applying for jobs.
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Data Analysis and Artificial Intelligence Methods
In this course, we write code in Python. However, the recorded online version also includes lectures for Excel, so you can choose to complete it using either tool.
What you'll learn: you'll cover the basics of statistics, understand concepts like median, correlation, and regression, and learn to identify patterns in data and test hypotheses using Python.
All of this will help you tackle the empirical part of your term papers or thesis more efficiently—and it will also come in handy when creating infographics.
Independent Assessment of Digital Competencies
- Digital Literacy
- Programming
- Data Analysis
Our Curriculum Developers and Teaching Staff
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Daria Kasyanenko
Deputy Head of Department, Senior Lecturer
Teaches: ‘Data Analysis in Python,’ ‘Python for Data Engineering,’ ‘ETL Processes,’ ‘DataOps,’ ‘NoSQL Databases,’ ‘Data Analytics, Artificial Intelligence, and Generative Models,’ ‘Fundamentals of Artificial Intelligence for Text Analysis.’
Education: Bachelor's and master's degrees from HSE University (Journalism and Media Communications), internship at KTH Royal Institute of Technology (Stockholm). ‘Education Analyst’ professional retraining (HSE Institute of Education).
Winner of Best Academic Supervisor in the ‘Student Attraction’ category (2024–2025). Participant in the HSE Professional Development Programme for Administrative Staff (2023).
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Mikhail Zhuravlev
Associate Professor, Senior Research Fellow, Academic Supervisor of the ‘Jurisprudence: Digital Lawyer’ programme
Teaches: ‘Information Law,’ ‘Digital Literacy,’ ‘Cyber Law: Data, Ethics, and Digital Property,’ ‘Research Seminar: Introduction to Digital Law,’ ‘Research Seminar: Law and Ethics of Artificial Intelligence.’
Education: Specialist and master's degrees from HSE University (Jurisprudence), Candidate of Legal Sciences (PhD equivalent, 2021).
Winner of Best Academic Supervisor in two categories (2025). Member of the High Professional Potential Groups in the ‘New Teachers’ category (2020–2021) and ‘New Researchers’ category (2014–2015).
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Maria Gordenko
Senior Lecturer
Teaches: ‘Algorithms and Data Structures,’ ‘Java and Object-Oriented Programming,’ ‘C++ Programming,’ ‘Python Programming Language,’ ‘Data Analysis Tools,’ ‘Neural Networks and No-Code Development of Digital Products.’
Education: Bachelor's and master's degrees from HSE University (Software Engineering), master's degree from Chelyabinsk State University (Jurisprudence).
Best Teacher award (2022, 2024–2025), Laureate of the Golden HSE award (2017), Best Academic Supervisor in two categories (2024–2025). Academic Supervisor of the master's programmes ‘Data Analysis in Development’ and ‘Financial Technologies.’
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Matvey Bakshuk
Lecturer
Has extensive experience teaching programming and data analysis to non-specialist students within the Data Culture project. Teaches: ‘Python Programming,’ ‘Basics of Data Analysis in International Relations,’ ‘Introduction to Data Science,’ ‘Introduction to Machine Learning,’ ‘Data Analytics, Artificial Intelligence, and Generative Models.’
Education: Bachelor's degree in Political Science from HSE University, master's degree in Economics from HSE University. Currently pursuing a PhD, with a dissertation on ‘Artificial Intelligence in Russian Higher Education: Factors of Use and Correlation with Student Academic Performance.’
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Maksim Karpov
Senior Lecturer, Junior Research Fellow at the LAMBDA Laboratory
Teaches Python courses at the Faculty of Computer Science's Continuing Education Centre.
Teaches: ‘Data Analysis in Python,’ ‘Data Analysis in Excel,’ ‘Data Analysis in Politics and Journalism,’ ‘Machine Learning,’ ‘Introduction to Deep Learning.’
After completing a specialist degree in International Relations, he earned a master's degree in Data Science.
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Tatiana Kazakova
Lecturer
Teaches: ‘Data Analysis in Python,’ ‘Introduction to Linguistics,’ ‘Computational Linguistics,’ ‘Programming and Linguistic Data,’ ‘Linguistics for Programmers,’ ‘Linguistic Aspects in NLP Context,’ ‘Linguistic Data: Quantitative Analysis and Visualisation.’
Education: Bachelor's degree in Fundamental and Applied Linguistics from HSE University.
Member of the High Professional Potential Group in the ‘New Researchers’ category (2023–2024). Has over 10 scientific publications and regularly presents at international linguistics conferences.
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Michael Nikityuk
Lecturer
Teaches: ‘Data Analysis,’ ‘Basics of Programming in Python,’ ‘Introduction to Python,’ ‘Data Analytics, Artificial Intelligence, and Generative Models.’
Education: Bachelor's degree in Economics from HSE University.
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Ilya Galushko
Teaches: ‘Data Analytics, Artificial Intelligence, and Generative Models,’ ‘Python Programming.’
Completed a master's degree in History from Moscow State University and is currently pursuing a PhD at the Department of Historical Informatics. His dissertation focuses on the effectiveness of stock market regulation in the Russian Empire in the early 20th century.
Received a research grant for a project on automating the annotation of archival documents using large language models (LLMs).
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Vasily Melnik
Senior Lecturer
Teaches: ‘Digital Literacy,’ ‘Law in Media Communications,’ ‘Legal Regulation of Media,’ ‘Research Seminar: Data Ethics and Responsible Use of Artificial Intelligence.’
Education: Bachelor's degree from Kutafin Moscow State Law University (MSAL), master's and PhD studies in Jurisprudence at HSE University. Candidate of Legal Sciences (PhD equivalent, 2024).
Best Teacher award (2023–2025), member of the High Professional Potential Group in the ‘New Teachers’ category (2024–2025).
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Aleksandr Klimov
Senior Lecturer, PhD student; Head of the Master’s programme ‘Digital Humanities’
Teaches: ‘Introduction to Linguistics,’ ‘Data Base Foundations’, ‘Fundamentals of Artificial Intelligence for Text Analysis,’ ‘Programming for Digital Humanities,’ ‘Data Analytics, Artificial Intelligence, and Generative Models.’
Education: Specialist degree in Philology from Cherepovets State University. Currently pursuing a PhD at HSE University with a dissertation on ‘The Evolution of Letter-Writing as a Literary Device in Russian Literature: A Comparative Analysis of Epistolary Novels, Embedded Letters, and the Epistolary Heritage of the 18th–19th Centuries.’
Best Teacher award (2024–2025), Best Academic Supervisor in two categories (2025).
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Tatiana Perevyshina
Lecturer
Teaches: ‘Data Analysis,’ ‘Data Analysis in Python,’ ‘Basics of Programming in Python,’ ‘Introduction to Data Analysis,’ ‘Introduction to Data Science,’ ‘Independent Test in Data Science, AI, and Generative Models’ (elementary, basic, and advanced levels).
She also serves as a methodologist for the data analysis track (elementary and basic levels).
Education: Bachelor's degree in Economics from HSE University, master's degree in Urban Planning from HSE University.
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Mikhail Hushchyn
Lecturer
Teaches: ‘Machine Learning 1,’ ‘Deep Learning,’ ‘Generative Models in Machine Learning.’
Education: Bachelor's and master's degrees from MIPT (Applied Mathematics and Physics), PhD from MIPT (Computer Science and Computer Engineering).
Best Teacher award (2024). Participant in the LHCb experiment at CERN (coordinator of the Machine Learning and Statistics working group). Author of several computer programs and MOOCs on machine learning.
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Assol Kubaeva
Lecturer
Teaches: ‘Basics of Programming in Python,’ ‘Python for Data Analysis,’ ‘Introduction to Data Analysis,’ ‘Mathematical Statistics and Data Analysis.’
Education: Bachelor's degree in Applied Mathematics and Computer Science from HSE University, master's degree in Economics from HSE University.
Best Teacher award (2025).
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Valeria Bozhenova
Senior Lecturer; Deputy Academic Supervisor of the ‘Digital Law’ programme
Teaches: ‘Digital Literacy,’ ‘Independent Digital Literacy Test,’ ‘Research Seminar: Legal Aspects of a Smart City’, ‘Research Seminar: Law and Ethics of Artificial Intelligence.’
She also serves as a methodologist for the digital literacy track.
Education: Master's degree in Jurisprudence from HSE University.
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Margarita Burova
Lecturer
Teaches: ‘Python Programming,’ ‘Data Analysis in Python,’ ‘AI and No-Code for Management: Automation of Routine,’ ‘Data Aggregation, Cleaning, and Parsing,’ ‘Advanced Data Analysis and Visualisation in Python.’
Education: Bachelor's degree in Psychology from HSE University, master's degree in Applied Mathematics and Computer Science from HSE University.
Best Teacher award (2024), Best Academic Supervisor in the ‘Student Digital Skills’ category (2024) and ‘Student Attraction’ category (2023).
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Ksenia Anisimova
Lecturer
Teaches: ‘Programming in Python,’ ‘Data Analysis,’ ‘Data Analytics, Artificial Intelligence, and Generative Models,’ ‘Intro to Programming in Python,’ ‘Artificial Intelligence in Communications.’
Education: Bachelor's degree from RSUH in Intelligent Systems in the Humanities, master's degree in Fundamental and Applied Linguistics from HSE University.
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Anastasia Parshina
Senior Lecturer
Teaches: ‘Programming in Python,’ ‘Introduction to Python,’ ‘Digital Humanities,’ ‘Independent Programming Test’ (elementary, basic, and advanced levels), ‘Research Seminar: Python Programming Basics.’
She also serves as a methodologist for the programming track.
Education: Bachelor's degree in Political Science from HSE University, master's degree in Sociology from HSE University.
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Anton Buzanov
Lecturer
Teaches: ‘Programming and Linguistic Data,’ ‘Theory of Language,’ ‘Linguistics for Programmers,’ ‘Linguistic Aspects in NLP Context,’ ‘Linguistic Data: Quantitative Analysis and Visualisation,’ ‘Typology.’
Education: Bachelor's degree in Fundamental and Applied Linguistics from HSE University. Currently pursuing a PhD, with a dissertation on ‘Rethinking the Theory of Possessive Constructions and Agreement with Anaphors.’
Best Teacher award (2025), Lyceum Students' Choice: Best Teacher (2024). Junior Research Fellow at the Laboratory for the Study and Preservation of Minority Languages, Institute of Linguistics, Russian Academy of Sciences.
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Yulia Moreva
Lecturer
Teaches: ‘Multilevel Modeling,’ ‘Automated Big Data Collection in Economic Sociology Studies,’ ‘Inequalities: Sociological Dimension,’ ‘Social and Political Attitudes,’ ‘Modern Social Theory for Quantitative Research.’
Education: Bachelor's degree from St Petersburg State University (Conflict Studies), two master's degrees (SPbU and HSE University), PhD in Sociology from HSE University (2025).
Junior Research Fellow at the Centre for Comparative Research on Social Well-Being. Member of the High Professional Potential Group in the ‘New Researchers’ category (2024–2025).
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Diana Susla
Lecturer
Teaches: ‘Data Analysis in Python,’ ‘Machine Learning 1,’ ‘Data Analytics, Artificial Intelligence, and Generative Models,’ ‘Programming and Computer Science,’ ‘Introduction to Deep Learning,’ ‘AI Automation in Media and Communication Workflows.’
She also serves as a methodologist for the data analysis track (basic level).
Faculty Success Stories
Ilya Galushko
‘I've been working with the Data Culture project for two years now, teaching programming and data analysis. By training, I'm a historian. Interestingly, I learned about Data Culture almost by accident while looking for a job in my field. I simply filled out a form on the project's website and passed the interview.
My main research interest is socio-economic history and the application of machine learning in the social sciences. Data Culture is a perfect fit for me: I get to stay in higher education, where I can pursue my research within the university environment; I work with the kind of audience that matters to me; and I teach subjects I'm genuinely passionate about. I truly enjoy introducing humanities students to the world of digital methods. What's more, in every group there are always some students who are deeply interested in this area as part of their academic specialisation—it's always a pleasure to teach people something that will truly benefit them in their research or professional careers.’
Ksenia Anisimova
‘My entire life has revolved around the space where mathematics meets the human or humanitarian side: a humanities-oriented class at the renowned mathematical State School 57, the Intelligent Systems department at RSUH, and a master's in Digital Humanities at HSE. Teaching Python to philology students a couple of times a week felt like the perfect gig—I'd always wanted to show humanities’ students the beauty of 'the other side.' And it seems that over my time at Data Culture, I've occasionally succeeded in doing just that.
What surprised me most about working at Data Culture is the sheer number of humanities students who, with great effort, managed to overcome their biases about computer science throughout the courses, and came to see these data programmes as a way to view the world from an entirely new perspective. Data Culture as a workplace is also quite unique. The people who develop and support the courses do an outstanding job. In short, it's all about the people—and they are truly remarkable on every side at HSE. I haven't stopped being amazed by them for years now.’
Alexander Klimov
‘I'm a linguist through and through in terms of my education, though very applied—my first degree is in translation, and my second is in computational linguistics. I teach a wide range of subjects: I am the head of the Master's programme “Digital Humanities,” lead specialised courses on thesis work and projects, and also teach some programming. It's interesting to see how the course is structured and the interaction between coordinators, instructors, teaching assistants, and students. It also gives me the chance to step outside the familiar linguist's environment and visit other departments—by teaching, we learn ourselves, so it's all very rewarding. I think what surprises everyone at Data Culture is the quality of the organisation. Almost seamless onboarding and workflows, well-thought-out materials—it's genuinely a pleasure to work here!’
