| Code |
Course Name |
Language |
Type |
| MAT 386E |
Computational Data Science |
English |
Elective |
| Local Credits |
ECTS |
Theoretical |
Tutorial |
Laboratory |
| 3 |
6 |
2 |
2 |
0 |
| Course Prerequisites and Class Restriction |
| Prerequisites |
MAT 244 MIN DD
or MAT 244E MIN DD
or MAT 242 MIN DD
or MAT 242E MIN DD
|
| Class Restriction |
None |
| Course Description |
| Big Data and project management. Statistical methods and machine learning in data science: Regression analysis and modelling,
basic classification and clustering methods.
Data warehousing and structure, data extraction, transform and loading (ETL).
Performance metrics and risk management. Big data platforms: Architecture (Hadoop, Spark), tools (MapReduce, Spark ML,
Kafka, Flink, Hive). Data processing methods. Sectoral applications. Model visualization and evaluation. Data Visualization and
reporting and interpretation of results. |
|
