Бакалавриат 2026/2027

Независимый экзамен по анализу данных, искусственному интеллекту и генеративным моделям. Базовый уровень

Статус: Курс обязательный (Международный бизнес)

Кто читает: Отдел развития цифровых компетенций

Где читается: Факультет компьютерных наук

Когда читается: 3-й курс, 4 модуль

Онлайн-часы: 2

Охват аудитории: для всех кампусов НИУ ВШЭ

Язык: английский

Кредиты: 1

Контактные часы: 2

Course Syllabus

Abstract

“Data Analysis and Artificial Intelligence Methods” is one of the digital competencies embedded in all educational programmes at HSE University (hereinafter, Data Analysis). It encompasses the use of mathematical methods and models to extract knowledge, solve professional problems, and develop new approaches. For each undergraduate field of study, the educational standard specifies the minimum required level of proficiency in this digital competency: Elementary, Intermediate, or Advanced. The Independent Examination in Data Analysis is a mandatory component of the curriculum for all undergraduate programmes. Its purpose is to confirm that students have attained the minimum level of competency required by their degree programme. The examination is administered after students have completed the courses designed to develop the relevant level of competency. This examination assesses Data Analysis competency at the intermediate level and is administered under proctored conditions. The final result is converted to a 10-point grading scale. Scores below 4 are rounded down to the nearest whole number, while scores of 4 or above are rounded to the nearest whole number. Failure to obtain a passing result in the Independent Examination in Data Analysis by the prescribed deadline constitutes academic failure.

Learning Objectives

• To develop skills in data processing, visualisation, and exploratory data analysis.
• To develop skills in formulating research questions and testing hypotheses using quantitative methods.
• To introduce students to linear and logistic regression.

Expected Learning Outcomes

• Selects appropriate charts for data visualization.
• Ability to select the appropriate type of visualization to solve a specific task.
• Ability to load data into software and work with it (filtering, aggregation, handling missing values).
• Ability to implement a loop with a condition and to represent input data in a format convenient for further processing.
• Ability to work with data structured as a dictionary and perform dictionary lookups.

Course Contents

• Exam DS Intermediate

Assessment Elements

• Part B
  Number of tasks: 3 Recommended completion time: 30 minutes
• Part C
  Number of tasks: 5 Recommended completion time: 60 minutes
• Part A
  Number of tasks: 10 Recommended completion time: 30 minutes

Interim Assessment

• 2026/2027 4th module
  0.3 * Part B + 0.2 * Part A + 0.5 * Part C

Bibliography

Recommended Core Bibliography

• Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011
• Kelleher, J. D., & Tierney, B. (2018). Data Science. The MIT Press.

Recommended Additional Bibliography

• Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.