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This master's program is designed to meet the growing need for data science professionals who can create advanced computational intelligence solutions, processing vast quantities of intricate data to guide corporate choices and commercial approaches.
A central focus of the curriculum is automated handling of big data and knowledge extraction using machine learning, neural networks, and evolutionary computation methods.
The course explores implementing state-of-the-art machine learning approaches for big data analysis, evaluating the statistical relevance of data mining outcomes, and executing sophisticated data mining operations. Students will gain exposure to key frameworks potentially including Hadoop MapReduce, Spark, relational database implementations, and NoSQL databases alongside user-friendly yet robust development platforms like Python and R.
The program investigates evolving concepts, methodologies, and administration of distributed intelligent computing systems through diverse case studies showcasing practical implementations, such as color steganography detection systems. Emphasis is placed on employing data science methodologies alongside computational intelligence for data-centric solutions, encompassing complex data analysis, interpretation, and visualization - skills increasingly sought after in sectors like marketing, pharmaceuticals, finance, healthcare, logistics, and business administration.
Participants may pursue an optional work placement opportunity² to enhance their competencies and improve career prospects. Additional details are available in the modules section.
2 years sandwich