The External Assessment of Digital Competencies (EADC) is embedded in all HSE University degree programmes and is mandatory for second-year undergraduate students enrolled in Russian-taught programmes. The EADC Final Assessment determines the student's final level of digital competency attainment.
The EADC Final Assessment is administered using proctoring procedures and lasts 180 minutes. It comprises a test component and a practical component. In the practical component, students address a problem scenario by selecting one of several possible courses of action.
The final result is converted to a 1-10 scale. Scores below 4 are rounded down by discarding the decimal part; scores of 4 or above are rounded to the nearest whole number. Failure to obtain a passing result in the External Assessment of Digital Competencies within the prescribed timeframe does not give rise to academic debt. However, failure to take the assessment is treated as academic debt until the assessment has been completed.
Learning Objectives
To determine the student’s final level of digital competence through test-based and practical assignments.
Expected Learning Outcomes
The student is able to apply digital competencies to analyse problem situations and select a well-reasoned course of action.
Course Contents
External Assessment of Digital Competencies
Assessment Elements
Part A
The test component comprises eight items, which may include multiple-choice questions, short-answer items, code-ordering tasks, and similar formats.
Part B
Part C
Interim Assessment
2026/2027 2nd module
0.4 * Part C + 0.25 * Part A + 0.35 * Part B
Bibliography
Recommended Core Bibliography
9781491912140 - Vanderplas, Jacob T. - Python Data Science Handbook : Essential Tools for Working with Data - 2016 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1425081 - nlebk - 1425081
Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.
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
Baesens, B. (2014). Analytics in a Big Data World : The Essential Guide to Data Science and Its Applications. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=761032
Kotu, V., & Deshpande, B. (2019). Data Science : Concepts and Practice (Vol. Second edition). Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1866160
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors