Practice Stage
Public syllabus for 2025-2026
Academic overview
Teaching team
Learning time distribution
| Total | ||||||
|---|---|---|---|---|---|---|
| Curriculum | Lecture | Practice | Total Weekly | Lecture | Practice | |
| 14 | 0 | 14 | 1 | 0 | 1 | |
| Exam hours | ||||||
| 0 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 112 | 0 | 0 | 112 | 0 | 0 | |
| Overall | ||||||
| 126 |
Learning outcomes
Knowledge
- (6a03a0952355ae3a04d2f310) Knowledge of statistical methods specific to different types of processing and understanding of how machine learning algorithms can be used;
- (6a03a0952355ae3a04d2f311) Understanding how large volumes of data can be processed in a distributed manner and the principles underlying high-performance computing;
- (C4) Understanding how platforms specific to big data processing work;
- (6a03a0952355ae3a04d2f313) Understanding how the computational complexity of an algorithm is determined and the specific requirements for scalability;
Skills
- (A1) Using concepts from computer science, mathematics, and statistics to define models and design strategies for data analysis and interpretation of results;
- (6a03a0952355ae3a04d2f316) Identifying statistical and machine learning techniques as well as appropriate IT tools for data processing and building decision models;
- (6a03a0952355ae3a04d2f317) Designing, implementing and testing software modules suitable for processing and analyzing large volumes of data;
- (A5) Using knowledge about building data-driven models to develop decision-support systems for different application domains.
Responsibility
- (6a03a0952355ae3a04d2f31b) Respecting the confidentiality of the employer and clients, but also protecting their intellectual property;
- (6a03a0952355ae3a04d2f31d) Responsibility to respect the highest professional standards in data processing;
- (6a03a0952355ae3a04d2f2fe) Maintaining autonomy, integrity and independence in professional opinions
- (R8) Ethical, honest, and collegial behavior in professional practice.
Online platform
Course content
| Content | Methods | Obs |
|---|---|---|
| - | - | - |
Course bibliography
(none)
Seminar content
| Content | Methods | Obs |
|---|---|---|
| - | - | - |
| Establishing an activity plan with objectives related to data collection, integration, and analysis workflows. Working with distributed data storage, cloud services, and streaming platforms. Establishing methodologies for ETL (extract-transform-load), big data ingestion, and scalable ML integration. Gathering, cleaning, organizing, and preprocessing structured/unstructured datasets. Developing, training, and deploying predictive analytics and recommendation models on large datasets. Evaluating scalability, accuracy, and efficiency of big data solutions. Documenting findings and preparing a final report with data-driven insights. The activity during the practice stage will be supervised by the tutor from the company who establishes the tasks to be executed by the student. Complete information about the practice stage: https://info.uvt.ro/practica/ | Problem analysis, dialogue | The activity schedule is flexible and may vary depending on the institution where the master's student is placed (an IT company relevant to the master's program). |
Seminar bibliography
The activity is carried out in collaboration with specialists in the field who work in relevant institutions.
Corroboration
(none)
AI tools guidance
Evaluation and delivery
| Activity | Criteria | Methods | Percentage |
|---|---|---|---|
| C |
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| S |
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| S |
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Performance standards
To pass the course, the student must cumulatively meet the following requirements: • completion of the full number of internship/practice stage hours required by the curriculum; • obtaining a positive evaluation from the mentor/supervisor within the host organization; • submission of the practice report written in accordance with the established requirements. Failure to meet any of the above conditions results in failing the course.
Additional info
(none)