Stagiu De Practica
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 | ||||||
| 2 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 134 | 25 | 82 | 18 | 9 | 0 | |
| Overall | ||||||
| 150 |
Learning outcomes
Knowledge
- • Advanced knowledge in large-scale data storage, distributed computing, and algorithmic analysis of big data.
- • Statistical and probabilistic models for large datasets.
- • Data mining, knowledge discovery, and pattern extraction.
- • Principles of big data system architecture and scalable application design.
Skills
- • Implementing machine learning and deep learning models on large datasets.
- • Using big data frameworks (e.g., Hadoop, Spark, Flink) and cloud-based platforms.
- • Applying data wrangling, cleaning, and preprocessing at scale.
- • Performing real-time stream processing and batch analytics.
Responsibility
- • Maintaining confidentiality and protecting intellectual property in collaborations.
- • Accurately representing one's level of competence and accepting tasks within its limits.
- • Preserving autonomy, integrity, and independence in professional opinions.
- • Promoting the integrity and reputation of the profession in line with the public interest.
- • Continuous professional development in the field.
Online platform
Course 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. | 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). |
Course bibliography
(none)
Seminar content
| Content | Methods | Obs |
|---|---|---|
| - | - | - |
| Bibliography: Specific documentation for the tools and software applications to be used The activity during the internship will be supervised by the tutor from the company who establishes the tasks to be executed by the student. Complete information about the internship: https://info.uvt.ro/cariere/stagii-de-practica/ |
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
The final grade is calculated as a weighted average of the grades corresponding to the two components. The grade for the tutor evaluation is assigned by the tutor from the institution where the internship takes place. Attendance rules for lectures and laboratories are in accordance with Chapter III, Article 19, Paragraph (3) of the Code of Student Rights and Obligations, and the Regulation on the Professional Activity of Students in the Bachelor's and Master's Study Cycles at the University of the West from Timișoara, 8th edition. Recovery of practical activities is carried out throughout the semester until week 11 by assigning additional assignments. A maximum of 3 practical activities can be recovered. Re-enrollment in the discipline occurs in accordance with Chapter III, Article 19, Paragraph (7) of the Code of Student Rights and Obligations, and the Regulation on the Professional Activity of Students in the Bachelor's and Master's Study Cycles at the University of the West from Timișoara.
Additional info
(none)