Data Analysis – Big Data. New post-graduate course available in UG offer

Na zdjęciu tabele, dane, wykresy i dwoje ludzi, którzy je analizują.

University of Gdańsk answers the growing demand for educating statistical data analysis professionals by launching new post-graduate course at the Faculty of Management, Data Analysis - Big Data.

Data Analysis – Big Data is a two semester, part-time, post-graduate, practical course held at the Department of Statistics of the UG Faculty of Management. The course is addressed to people working or willing to start working at statistical data analysis positions (especially large data volumes)

Information on 2020/2021 University of Gdańsk Admissions

Big data

Volume, variety and velocity are three defining properties of big data. It requires efficient, innovative processing methods to extract new knowledge that could, for instance, help make proper decision or automate processes in the company. Big data can include both structured (like numeric table) and unstructured data like writing, an image or a movie clip. Extracting knowledge from such datasets is only possible after proper processing, analysis and visualization. Statisticians and IT professionals play a key role in this process.

Multiple enterprises see the lack of well-educated, talented data analysts as the main obstacle in realizing full potential of large data volumes analyses. Multiple companies launch dedicated programs to create data-driven cultures. However, there is still a shortage of professionals and managers able to forge data analysis findings into business actions in real life.

New challenges for business

Multiple enterprises use big data now, other plan in the future. Data Analysis – Big Data course will showcase multiple benefits of large data volumes analysis.

Course of study information

The curriculum involves 228 hours of classes held at computer laboratories of the UG Faculty of Management in Sopot.

During the course, students will learn how to prepare data analysis, data exploration, build statistical models and use advanced methods of data mining with the use of advanced programs (mainly in R and Python programming languages).


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