In this course you will learn basic concepts in Cloud Computing, including how to write your own software using key cloud programming models and tools to support data mining and data analysis applications.
This course is structured with a mix of recorded lectures, programming labs and forum discussions.
B649 Cloud Computing online is a programming intensive course. It has similar requirements to the CS graduate level residential version. Students are expected to have weekly (or biweekly) programming homework. General programming experience with Windows or Linux using Java (2-3 years) and scripts is required. A background in parallel and cluster computing is a plus, although not necessary.
Judy Qiu is an Assistant Professor in the School of Informatics and Computing at Indiana University. Her research interests focus on data-intensive computing at the intersection of cloud and multicore technologies, with an emphasis on life science applications using MapReduce as well as traditional parallel and distributed computing approaches.
The course progress is the percentage of mandatory items completed for the course.
| Exams (40%) - Midterm (20%), Final (20%) |
| Written Assignments (30%) - 5 Quizzes (each 2 points), 4 Homework assignments(each 5 points) |
| Projects (30%) - 6 Projects: [Hadoop WordCount (5), Hadoop PageRank (5), Hadoop Blast (10), HBase WordCount (5), Building an Inverted Index (10), Build a Search Engine (20)], (2 Optional Projects: Twister/Giraph PageRank (20), Twister K-means (20)) |
Week 1 - Jan 27th to Feb 2nd
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Week 2 - Feb 3rd to Feb 9th
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Week 3 - Feb 10th to Feb 16th
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Week 4 - Feb 16th to Feb 23th
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Week 5 - Feb 24th to Mar 2nd
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Week 6 - Mar 3rd to Mar 9th
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Week 7 - Mar 10th to Mar 16th
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Week 8 - Mar 17th to Mar 23rd
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Week 9 - Mar 24th to Mar 30th
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Week 10 - Mar 31st to Apr 6th
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Week 11 - Apr 7th to Apr 13th
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Week 12 - Apr 14th to Apr 20th
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Week 13 - Apr 21st to Apr 27th
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Week 14 - Apr 28th to May 4th
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At the end of this course, you will have learned key concepts in cloud computing and enough programming to be able to solve data analysis problems on your own.
The class has several projects that will allow students to get firsthand experience with the technologies taught here. Projects are performed on VirtualBox Appliances or academic clouds like FutureSystems.