Academy of Technical and
Art Applied Studies Belgrade

Data Analysis Course code: 170717 | 6 ECTS credits

Basic information
Level of Studies:
Year of Study: 3
Semester: 6
Requirements: Knowledge of basics of progrmming, databases and computer networks
Goal: The goal of the course is to train students to independently solve current problems in the field of data analysis. The concepts of knowledge discovery in large amounts of data are analyzed in particular
Outcome: After the course is over, the students will be able to independently create data analysis infrastructure using open-source software and apply tools for analysis and data processing.
Contents of the course
Theoretical instruction:
  1. Introductory lecture. Program, organization and content of the subject. Relation with other courses.
  2. Introduction to the architecture of the data analysis system.
  3. NoSQL databases.
  4. Infrastructure of the data processing system.
  5. Data warehouse.
  6. Application types and data analysis tools.
  7. Methods and algorithms for discovering knowledge in data.
  8. Queries over large amounts of data.
  9. Reporting systems and tools.
  10. Analysis of structured and unstructured data. Review results.
  11. Computer clusters. Concept and practical application.
  12. Security and data integrity.
Practical instruction (Problem solving sessions/Lab work/Practical training):
  1. Laboratory classes are followed by lectures, where students solve practical problems in the field of data analysis using available libraries. To train course participants for the development of complete solutions for processing and analyzing structured and unstructured data using open-source software
Textbooks and References
  1. D. Letić, MathCad 13 u matematici i vizuelizaciji, Kompjuter biblioteka, 2007.
  2. B.Marr, Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, Wiley, 2015.
  3. M. Despotović-Zrakić, V.Milutinović, A.Belić, Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education, IGI Global, 2014.
  4. M.Minelli, M.Chambers,A.Dhiraj, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, Wiley, 2013.
Number of active classes (weekly)
Lectures: 2
Practical classes: 3
Other types of classes: 0
Grading (maximum number of points: 100)
Pre-exam obligations
Points
activities during lectures
10
activities on practial excersises
30
seminary work
0
colloquium
30
Final exam
Points
Written exam
30
Oral exam
0