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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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To teach students some key technologies, platforms, tools and systems used in big data Big Data management and analysis.
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Course Content
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Big Data, Distributed Computing, Cluster Computing, Scalable Machine Learning, Cloud Computing and Virtualization, Graph Analytics, Data and Ethics.
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Course Methods and Techniques
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Lecture, Problem solving, Experiment
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Prerequisites and co-requisities
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( BBM102 ) and ( BBM104 ) and ( BBM467 )
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. Fuat Akal
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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?Mining of Massive Datasets (2nd Edition)?, Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author), Cambridge University Press, 2014.
?Learning Spark: Lightning-Fast Big Data Analysis?, Holden Karau and Andy Konwinski, O'Reilly, 2015.
?Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale?, Jan Kunigk, Ian Buss, Paul Wilkinson and Lars George, O'Reilly, 2019.
?Cloud Computing: A Practical Approach?, Robert C. Elsenpeter and Toby Velte, McGraw-hill, 2009.
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Course Notes
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“Mining of Massive Datasets (2nd Edition)”, Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author), Cambridge University Press, 2014.
“Learning Spark: Lightning-Fast Big Data Analysis”, Holden Karau and Andy Konwinski, O'Reilly, 2015.
“Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale”, Jan Kunigk, Ian Buss, Paul Wilkinson and Lars George, O'Reilly, 2019.
“Cloud Computing: A Practical Approach”, Robert C. Elsenpeter and Toby Velte, McGraw-hill, 2009.
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