Project Levels
Each undergraduate programme has two options to improve students’ data skills: it can either launch a new course or upgrade an existing one. Since HSE University offers training in a wide variety of fields, the ultimate approach depends on the profile of the given group of degree programmes whose students may share a similar level of data science literacy. Thus, each group of programmes features its own set of Data Culture courses. A set of courses is shaped on the basis of the needs of a specific academic field, while a group of degree programmes may feature common compulsory courses and electives with in-depth instruction in data science.
Beginner level
Data Analysis skills
- Knowledge of data types and methods for their presentation;
- Elementary activities with numerical data, e.g., descriptive statistics, visualizations relying on basic diagrams;
- Carrying out basic analysis of text data, e.g., using regular expressions for text processing;
Programming skills
- Python: basic data types and syntax;
- Ability to read and understand someone else's code and apply it in tasks;
- Ability to write a programming code using basic control constructs (e.g., branching, cycles) and programming languages of simple functionality (input-output, files operations, basic data structures);
- Ability to write a programming code based on step-by-step algorithm for more complicated tasks;
- Ability to automate simple routine tasks: processing of large collections of files, basic calculations, frequency analysis of texts, etc.
Basic level
Data Analysis skills
- Ability to apply statistical methods to process data, identify patterns, test hypotheses and make decisions;
- Ability to apply machine-learning methods (in basic form, without additional processing or modification) for solving practical data analysis tasks;
- Competence in a full range of skills for visualizing data.
Programming skills
- Competence in functional properties of programming language and their instruments for text and spreadsheet data processing;
- Ability to decompose tasks into separate blocks and combine basic constructions of programming languages for their implementation;
- Ability to collect data from databases and online sources (with the use of open APIs and processing of unstructured data);
Advanced level
Data Analysis skills
- Ability to carry out a full cycle of task-solving relying on machine-learning technologies and advanced analysis approaches: data processing, development of features, selection of qualitative metrics, selection and teaching of a model, model validation, etc;
- Understanding of the principles for intellectual data analysis methods and ability to adapt them with consideration of data specifics, quality criteria, model requirements (e.g. interpretability, response rate, etc.);
- Ability to visualize analytical results and develop models using web-apps or other instruments;
Programming skills
- Ability to write effective code with the application of specialized algorithms and data structures;
- Possession of basic skills in software engineering (testing and coding, structuring code);
- Web-app development skills;
Professional level
* This level could be achieved only within the framework of certain specializations or an individual educational trajectory.
Skills and knowledge
- Knowledge of the theoretical foundations of contemporary machine learning;
- Knowledge of specialized machine-learning and data processing methods, which can be applied in one’s professional area (e.g., processing signals, neural-network methods and deep learning, computer vision, and natural language processing, etc.) and an understanding of the specifics for their application;
- Ability to achieve data analysis objectives in other subject areas with the support of experts;
Expert level
* This level could be achieved only within the framework of certain specializations or an individual educational trajectory.
Skills and knowledge
- Knowledge of the contemporary data science and relevant fields;
- Ability to research in the sphere of data science; develop new data analysis methods.
| Digital literacy | Programming | Data Analisis | Overall Level of DS skills: Beginner |
| one level for all students | beginner | beginner | |
| beginner | basic and above | ||
| basic and above | beginner |
Для наращивания навыков в области DataScienceкаждой образовательной программе бакалавриата предлагаются новые курсы или перерабатываются уже существующие. В связи с большим разнообразием направлений подготовки Вышки, предлагаемые решения адаптируются для блоков программ, сопоставимых по уровню подготовки студентов.
Для каждого блока предлагается своя система курсов DataCulture. Эти системы курсов определяются спецификой предметных областей и для блоков программ могут иметь общие обязательные курсы и общие выборные курсы для более серьёзной подготовки в области наук о данных.
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