Course code 07 79 2010 19
Number of ECTS points 6
Course title in the language of instruction 
Przetwarzanie i analiza dużych zbiorów danych
Course title in Polish Przetwarzanie i analiza dużych zbiorów danych
Course title in English 
Big Data Processing And Analysis
Language of instruction Polish
Form of classes
Lecture Tutorials Laboratory Project Seminar Other Total of teaching hours during semester
Contact hours 10 20 10 0 40
E-learning No No No No Yes No
Assessment criteria (weightage) 0.45 0.45 0.10 0.00
Unit running the course Instytut Informatyki
Course coordinator dr hab. inż. Agnieszka Wosiak
Course instructors dr inż. Marcin Kwapisz, dr inż. Jan Rogowski, dr hab. inż. Agnieszka Wosiak
Prerequisites 
Database fundamentals
Fundamentals of programming languages
Course learning outcomes
  1. A student who completed the course can characterize large volumes of data and describe techniques of big data processing and analysis.
  2. A student who completed the course can apply algorithms to search and mine large data sets.
  3. A student who completed the course can select the appropriate development tools for processing and analysis of large data sets.
Programme learning outcomes
  1. the graduate demonstrates knowledge and understanding of the main development trends in computer science and, to an increased extent, selected facts, objects and phenomena constituting advanced general knowledge, as well as methods and theories that underlie the complex relationships between them, and selected advanced detailed issues in the field of computer science;
Programme content The course covers issues related to techniques of big data processing and analysis and enabling the students to acquire the skills to use appropriate methods of big data processing and analysis.
Assessment methods 
written exam (learning outcome 1.)
discussion (learning outcomes 1., 2. and 3.)
practical task (learning outcomes 2. and 3.)
written report (learning outcomes 2. and 3.)
Grading policies Lecture+seminar: Written exam. Laboratories: laboratory tasks and laboratory test.
Course content LECTURE 1. Characteristics of large volumes of data (Big Data) and their impact on existing analytical solutions. 2. Modern solutions used for the transmission, storage and processing of large data sets. 3. The architecture of modern systems for processing based on the example of Hadoop platform. 4. Algorithms for processing large data sets. 5. Multidimensionality reduction. 6. Data processing on a large scale using MapReduce paradigm. 7. Programming languages for searching and processing of large data sets. 8. Data streams and their analysis. LABORATORY Laboratory tasks related to the lecture issues assigned by the teacher.
Basic reference materials 
  1. Loshin D.: Big Data Analytics. From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann, 2013
  2. Stanton J.M.: Introduction to Data Science, E-book, 2013
  3. Rajaraman A., Ullman J.D.: Mining of Massive Datasets, Cambridge University Press, 2011
  4. Rachel Schutt,Cathy O'Neil: Badanie danych. Raport z pierwszej linii działań, O'Reilly, 2014
  5. White T., Hadoop: The Definitive Guide, 3rd Edition, O'Reilly Media / Yahoo Press, 2012
Other reference materials 
  1. Morzy T.: Eksploracja danych. Metody i algorytmy, PWN, Warszawa, 2013
  2. Dunning T., Friedman E.: Time Series Databases, O'Reilly, 2014
  3. Pramod J. Sadalage,Martin Fowler: NoSQL. Kompendium wiedzy, Helion, 2014
  4. Lin J., Dyer C.: Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010
Average student workload outside classroom 
126
Comments 

                    

                    

                        Updated on 
                        2019-06-07 11:52:46
                    

                    

                        Archival course yes/no
                        no