General information
Study description
In the first two decades of the 21st century, a surge in demand for the analysis of large data sets could be observed. The development of Internet and information and communication technologies, combined with the qualitative change taking place in technology, science, life sciences, economics, social sciences, as well as medicine - has resulted in the possibility of collecting data of terabytes or even petabytes for the study of individual cases. Such volumes of data are impossible to analyze by classical methods.
On the other hand, the rules and the information they can contain - when properly used - can make a significant contribution to improving the quality of life of an individual, an individual as a patient, a society, a corporate organization; finally, they can be crucial in discovering new laws of nature and the structure of matter on both the micro and macro world scales. With the increase in the volume of collected data, we are seeing the development of a new science known as Data Science, and with technological developments, opportunities are being created to analyze them using unconventional methods of engineering large data sets known as Big Data.
Opening hours
Monday 8am-5pm
Tuesday 8am-5pm
Wednesday 8am-5pm
Thursday 8am-5pm
Friday 8am-4pm
Contact
Office of Graduate Studies
room B 17
tel. (+48) 512 497 506
tel. (+48) 504 640 530
tel. (+48) 22 58 44 597
e-mail: podyplomowe@pja.edu.pl
ul. Koszykowa 86
02-008 Warszawa
Centre for Postgraduate Studies
The help for candidates at PJAIT is provided stationary, by phone and by e-mail.
Documents can be delivered electronically - uploading scans to your individual recruitment account.
Information on the processing of personal data of college recruiters at the PJAIT
Head of Postgraduate Studies in Big Data - Big Data Engineering:
Prof. dr hab. Grzegorz Marcin Wójcik
e-mail: gmwojcik@pjwstk.edu.pl
Who are the studies aimed at?

Postgraduate students will learn about leading technologies used in big data analysis, both free and commercial implementations.
The main focus will be on Apache open technologies Cassandra, HBase, MLlib, Spark, Mahaut. In addition, the capabilities of Microsoft Azure Machine Learning cloud computing will be presented and the technologies offered by Google Cloud and Viya platforms within SAS Cloud Analytics Services will be outlined. Particular emphasis will be placed on enhancing competencies in Python and R programming for big data analytics applications. In addition, students will learn the theory and apply advanced machine learning algorithms in practice.
- The postgraduate program is aimed at those wishing to gain competence in analyzing and processing large amounts of data for business, science, medicine and other spaces of activity for use as decision support tools.
- Candidates for postgraduate studies should be graduates of bachelor's or master's degree in computer science or related fields, economics, engineering. Basic knowledge of: any programming language (e.g. Python, Java, C++), use of UNIX class systems (e.g. Linux, Solaris, macOS), relational database theory, statistical methods is recommended. Knowledge of English at least at the B2 level is required.
- The degree program is designed for both computer scientists and professionals from other fields who want to apply solutions of Data Science in its broadest sense to support their daily professional work in particular decision-making.

Meeting with managers of postgraduate programs
No one will tell you more about the studies offered by Centre for Postgraduate Studies than those who created them. We invite you to watch and listen to the interviews with the managers of the postgraduate and MBA programs (including the head of CKP Ms. Marta Godzisz), conducted by Ms. Aleksandra Szyr.
