Бакалавриат
2025/2026
Независимый экзамен по анализу данных, искусственному интеллекту и генеративным моделям. Начальный уровень
Статус:
Курс по выбору (Иностранные языки и межкультурная коммуникация)
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
Контактные часы:
2
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 elementary 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
- Developing skills in reading, interpreting, and evaluating the quality of quantitative data analysis and presentation. Building the ability to assess the quality and correctness of data visualizations. Enhancing skills in formulating research questions and solving them using quantitative methods. Acquiring and strengthening competencies in using specialized libraries and software for data collection, processing, visualization, and analysis.
Expected Learning Outcomes
- Select an appropriate type of data visualization for a given dataset.
- Identify errors in data visualizations (such as distorted trends or misleading information) and avoid them when creating their own visualizations.
- Ability to interpret the results of simple experimental studies and surveys.
- Ability to accurately understand basic statistical terminology
- Ability to determine the distribution based on descriptive statistics
- Ability to choose and calculate the appropriate correlation coefficient (Pearson or Spearman)
- Ability to calculate linear regression quality metrics (MSE, MAE, R2)
Assessment Elements
- Part A10 tasks, time allotted – 20 minutes
- Part B3 tasks, time allotted – 15 minutes
- Part C8 tasks, time allotted – 45 minutes
Bibliography
Recommended Core Bibliography
- Hector Guerrero. (2019). Excel Data Analysis. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.3.030.01279.3
- Introduction to Statistics and Data Analysis, With Exercises, Solutions and Applications in R, Christian Heumann, Michael Schomaker, Shalabh, Springer Nature Switzerland AG 2022, 978-3-031-11833-3, published: 30 January 2023
- Pandas for everyone : Python data analysis, Chen, D. Y., 2023
Recommended Additional Bibliography
- Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.