BIG DATA – DATA SCIENCE, ANALYTICS AND TECHNOLOGIES
Our aim is developing skills in the design of efficient and robust models for statistical analysis of data, design implementing and using data mining algorithms, in using technologies for big data processing and in implementing scalable applications.
Topics:
- TopicsBig Data Technologies, Data Warehouses, Cloud Computing;
- Data Mining, Machine Learning, Data Analysis and Programming in R;
- Probabilistic Models for Data Science, Predictive Models and Analytics, Optimization, Biostatistics.
Skills:
- Informatics/Computer Science, Mathematics and informatics, Mathematics, Applied Mathematics, Economics;
- Computer Engineering, Information Technologies;
- Other programs on scientific or engineering fields.
Career opportunities:
- (Big) Data Scientist;
- (Big) Data Analyst;
- (Big) Data Engineer;
- (Big) Data Architect.
Year I Semester I
| Topics |
Nr. Credits |
Lectures / week |
Seminaries or Laboratories / week |
| Probabilistic Models for Data |
6 |
2 |
1 |
| Data Analysis and Programming in R |
6 |
1 |
2 |
| Operations Research and Optimization |
6 |
2 |
1 |
| Ethics and Academic Integrity |
2 |
1 |
– |
| Elective 1.1 |
5 |
2 |
1 |
| Elective 1.2 |
5 |
2 |
1 |
| TOTAL |
30 |
10 |
6 |
| Electives – package 1.1 |
| Distributed Systems |
|
|
|
| Advanced Logic for Functional Programming |
|
|
|
| Optional Subjects – package 1.2 |
| Distributed Methods and Technologies based on XML |
|
|
|
| Fuzzy Modeling for Data Science |
|
|
|
| Additional Topics (from undergraduate curricula) |
| Databases |
5 |
2 |
2 |
| Programming I |
6 |
2 |
2 |
| Programming II |
5 |
2 |
2 |
Year I Semester II
| Topics |
Nr. Credits |
Lectures / week |
Seminaries or Laboratories / week |
| Data Warehouses |
5 |
1 |
2 |
| Data Mining |
5 |
2 |
1 |
| Big Data Technologies |
5 |
1 |
2 |
| Intership |
5 |
– |
1 |
| Elective 2.1 |
5 |
2 |
1 |
| Elective 2.2 |
5 |
2 |
1 |
| TOTAL |
30 |
8 |
8 |
| Electives – package 2.1 |
| Parallel Computing |
|
|
|
| Predictive Models and Analytics |
|
|
|
| Electives – package 2.2 |
| Parallel Computing |
|
|
|
| Predictive Models and Analytics |
|
|
|
| Electives – package 2.2 |
| Dynamical Systems in Machine Learning |
|
|
|
| Biostatistics and Medical Data Analysis |
|
|
|
| Additional topics (from undergraduate curricula) |
| Programming II |
6 |
2 |
2 |
Year II Semester I
| Topics |
Nr. Credits |
Lectures / week |
Seminaries or Laboratories / week |
| Machine Learning |
6 |
2 |
1 |
| Big Data Applications |
6 |
1 |
2 |
| Data Science Industry Project |
6 |
– |
3 |
| Elective 3.1 |
6 |
2 |
1 |
| Elective 3.2 |
6 |
2 |
1 |
| TOTAL |
30 |
7 |
8 |
| Electives – package 3.1 |
| Computer Vision |
|
2 |
1 |
| Statistical Methods for Clinical Studies |
|
2 |
1 |
| Electives – package 3.2 |
| Metaaheuristic Algorithms |
|
2 |
1 |
| Test Mining |
|
2 |
1 |
| Special Topics in Artificial Intelligence |
|
2 |
1 |
Year II Semester II
| Topics |
Nr. Credits |
Lectures / week |
Seminaries or Laboratories / week |
| Research and Professional Practice |
8 |
|
3 |
| MSc Thesis Preparation |
15 |
|
8 |
| Sciencific Seminar |
7 |
|
3 |
| TOTAL |
30 |
– |
14 |
