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