Бакалавриат
2025/2026
Независимый экзамен по анализу данных, искусственному интеллекту и генеративным моделям. Продвинутый уровень
Статус:
Курс обязательный (Управление цифровым продуктом)
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
Преподаватели:
Перевышина Татьяна Олеговна
Язык:
английский
Кредиты:
1
Course Syllabus
Abstract
For each bachelor's degree course, the educational standard defines the minimum required level of mastering this digital competence: Elementary/Intermediate/Advanced. Independent Data Science Test. is a mandatory part of the curriculum for all Bachelor's degree programs. It assumes confirmation of the minimum required level for the development of this competence. The assessment is carried out after the courses that ensure the formation of this level have been completed at the Undergraduate Program. This exam checks the availability of competence in Data Analysis at the Advanced level. The final result is translated into a scale from 1 to 10. A score below 4 points is rounded off with the fractional part dropped (to the smallest integer), a score below 4 points is rounded to the nearest integer.The absence of positive results of the Independent Data Science Test. within the established time limits entails academic debt.
Learning Objectives
- The advanced-level exam covers topics in linear algebra, probability theory, statistics, data analysis, and machine learning.
Expected Learning Outcomes
- Selects appropriate charts for data visualization.
- Ability to train a model, tune its hyperparameters, select the best model for a given task, and evaluate its performance.
- Theoretical principles behind a nonlinear machine learning algorithm
- Ability to compute an error metric using a given formula or algorithm.
- Ability to identify the type of machine learning task
Assessment Elements
- Part ATest Section (Part A) Multiple-choice questions with short prompts. Recommended completion time: 60 minutes
- Part BTasks (Part B) Open-ended questions (no answer choices provided). Recommended completion time: 30 minutes
- Part CWorking with a Dataset (Part C) Recommended completion time: 60 minutes
Bibliography
Recommended Core Bibliography
- A first course in machine learning, Rogers, S., 2012
- Foundations of machine learning, Mohri, M., 2012
- Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.
Recommended Additional Bibliography
- A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62