Bachelor
2025/2026
Data Science, AI and Generative Models Independent Test. Intermediate
Type:
Compulsory course (International Programme 'International Relations and Global Studies')
Delivered by:
Digital Skills Development Unit
Where:
Faculty of Computer Science
When:
3 year, 4 module
Online hours:
2
Open to:
students of all HSE University campuses
Instructors:
Tatiana Perevyshina
Language:
English
ECTS credits:
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 Intermediate 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 data handling skills: data processing, visualization, and exploratory data analysis. - Building skills in formulating research questions and testing hypotheses using quantitative methods. - Introduction to linear and logistic regression tasks.
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.
Assessment Elements
- Part A10 tasks Recommended completion time: 30 minutes
- Part B3 tasks Recommended completion time: 30 minutes
- Part C5 tasks Recommended completion time: 60 minutes
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.