Course Code |
Course Title |
Weekly Hours* |
ECTS |
Weekly Class Schedule |
T |
P |
AID304 |
Big Data Analytics |
3 |
2 |
6 |
|
Prerequisite |
None |
It is a prerequisite to |
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Lecturer |
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Office Hours / Room / Phone |
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E-mail |
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Assistant |
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Assistant E-mail |
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Course Objectives |
• Provide an overview of key platforms like Hadoop, Spark, and other relevant tools.
• Discuss various methods of storing data and explain the processes of uploading, distributing, and processing data.
• Explore diverse approaches for implementing analytics algorithms on different platforms.
• Delve into the challenges related to visualization and mobile integration in the context of Big Data Analytics.
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Textbook |
Raj Kamal and Preeti Saxena, “Big Data Analytics Introduction to Hadoop, Spark, and Machine Learning”, McGraw Hill Education, 2018 ISBN: 9789353164966, 9353164966 |
Additional Literature |
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Learning Outcomes |
After successful completion of the course, the student will be able to: |
- Acquire knowledge in the field of analyzing Big Data.
- Obtain understanding regarding the suitable tools, algorithms, and platforms to utilize for different real-world use cases.
- Gain practical experience in addressing Analytics, Mobile, Social, and Security challenges associated with Big Data..
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Teaching Methods |
The course will commence with a one-hour session dedicated to theoretical concepts and providing a comprehensive understanding of the topic's background. Subsequently, we will transition to hands-on programming and practical exercises. |
Teaching Method Delivery |
Face-to-face |
Teaching Method Delivery Notes |
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WEEK |
TOPIC |
REFERENCE |
Week 1 |
Introduction of Big Data Analytics |
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Week 2 |
Big Data Platforms |
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Week 3 |
Big Data Platforms |
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Week 4 |
Big Data Platforms |
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Week 5 |
Big Data Analytics Algorithms |
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Week 6 |
Big Data Analytics Algorithms |
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Week 7 |
Big Data Analytics Algorithms |
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Week 8 |
Real-Time Stream Analysis |
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Week 9 |
Streaming and Linked Big Data Analysis |
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Week 10 |
Big Data Visualization |
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Week 11 |
Big Data Visualization |
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Week 12 |
Data Visualization and Graph Database |
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Week 13 |
Big Data Analytics Applications |
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Week 14 |
Big Data Analytics Applications |
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Week 15 |
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