Bachelor 2026/2027

Data Science, AI and Generative Models Independent Test. Advanced Level

ID 1109681

Type: Compulsory course (Data Science and Business Analytics)

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

Language: English

ECTS credits: 1

Contact hours: 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 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

Course Contents

• Independent Data Science Test. Advanced Level

Assessment Elements

• Part A
  Short multiple-choice questions (15 questions worth 0.2 points each)
• Part B
  Questions without answer options (5 questions worth 0.6 points each)
• Part C
  Five dataset-based tasks with individual weightings

Interim Assessment

• 2026/2027 4th module
  Multiple-choice component × 0.2 (Part A) + Problems × 0.3 (Part B) + Dataset task × 0.4 (Part C)

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

Authors

• SUSLA DIANA MIKHAYLOVNA