BIG DATA – DATA SCIENCE, ANALYTICS AND TECHNOLOGIES

Which is the main aim?

The main aim of the Big Data master programme is to offer knowledge and skills such that the graduates are able to
  • design efficient and robust models for statistical analysis of data,
  • implement and use data mining algorithms,
  • use technologies for big data processing,
  • implement scalable applications.
What topics are studied?
  • Big 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;
  • Text Mining, Computer Vision, Metaheuristic Algorithms
Which is the target group? To this MSc program can apply graduates of:
  • Informatics/Computer Science, Mathematics and informatics, Mathematics, Applied Mathematics, Economics;
  • Computer Engineering, Information Technologies;
  • Other programs on scientific or engineering fields.
Which are the career opportunities?
  • (Big) Data Scientist;
  • (Big) Data Analyst;
  • (Big) Data Engineer;
  • (Big) Data Architect
* Note: for syllabus click on the lecture name

Year I Semester I

Topics
ECTS
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 Logical 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 III
5
2
2

Year I Semester II

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Data Warehouses
5
1
2
Data Mining
5
2
1
Big Data Technologies
5
1
2
Internship
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
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
ECTS
Lectures / week
Seminaries or Laboratories / week
Machine Learning
6
2
1
Big Data Applications
6
1
2
Data Processing 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
Metaheuristic Algorithms
2
1
Text Mining
2
1
Special Topics in Artificial Intelligence
2
1

Year II Semester II

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Research and Professional Practice
8
3
MSc Thesis Preparation
15
8
Sciencific Seminar
7
3
TOTAL
30
–
14