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The course Introduction to Big Data aims to introduce the concepts of storing, managing, querying and analyzing huge amounts of data. The main topics of the course are: computer clusters, programming for Big Data, cloud computing, management of Big Data, No Sql databases, big data analytics, big data sources (social networks, semantic web, biomedical databases). |
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Programming techniques and databases. |
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The main goal of the course is to provide the students with a deep knowledge on methods and technologies for storing, managing, and querying Big Data. Big Data are collections of huge amounts of non structured data coming from heterogeneous sources. These are not processable with traditional computing systems. Finally, the course is going to investigate hardware and software components for big data management. |
The course is going to analyze different aspects about big data. In particular, it will deepen following topics: • Dimensional modelling
• Hadoop and MapReduce
• Hybrid systems
• No Sql databases and in memory dbs
• Programming with R
• Machine Learning
• Social Analytics
• Marketing analytics
• Biomedical analytics
• Data visualization
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Big Data Related Technologies, Challenges and Future Prospects. Min Chen, Shiwen Mao, Yin Zhang, Victor C.M. Leung. Springer, 2014
Big Data Principles and best practices of scalable realtime data systems. Nathan Marz and James Warren, 2015
Big Data Analytics with R and Hadoop. Vignesh Prajapati, 2013
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The exercises will cover all topics presented during the course. |
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Βοηθός/Καθηγητή Περιοχή Καθηγητή
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Λίστα μαγνητοσκοπημένων παραδόσεων |