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.
Course Contents
Exam DS Intermediate
Assessment Elements
Part A
10 tasks
Recommended completion time: 30 minutes
Part B
3 tasks
Recommended completion time: 30 minutes
Part C
5 tasks
Recommended completion time: 60 minutes
Interim Assessment
2025/2026 4th module
0.4 * Part C + 0.4 * Part B + 0.2 * Part A
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.
Преподаватель
Перевышина Татьяна Олеговна
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors